Methods and systems for adjusting tumor mutation burden by tumor fraction and coverage

Abstract

Provided herein is a method for detecting a tumor mutation load (tumor mutational burden, TMB) of a subject. In one aspect, the method includes determining the observed mutation count according to the sequence information obtained from the nucleic acid in the sample from the subject, and determining the coverage of the tumor score and / or nucleic acid to generate sequencing parameters. The method also includes determining the expected distribution of the expected mutation score and / or the expected mutation score to generate an expected result in the case of given sequencing parameters, and adjusting the observed mutation count to generate the result of the adjustment in the case of given expected results, thereby detecting the TMB of the subject. Other aspects relate to a method for selecting a customized treatment for the cancer of the subject, and a method for treating the cancer of the subject. Other aspects include a related system and a computer-readable medium for detecting the TMB of the subject.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and is based on the filing dates of the following U.S. provisional patent applications: No. 62 / 702,280 filed on July 23, 2018; No. 62 / 741,770 filed on October 5, 2018; No. 62 / 782,894 filed on December 20, 2018; and No. 62 / 824,246 filed on March 26, 2019, the entire disclosures of which are incorporated herein by reference.

[0003] background

[0004] A tumor is an abnormal growth of cells. For example, when normal cells and / or cancer cells die, DNA is often released into body fluids as cell-free DNA and / or circulating tumor DNA. Tumors can be benign or malignant. Malignant tumors are often called cancers.

[0005] Cancer is the leading cause of disease worldwide. Every year, tens of millions of people around the world are diagnosed with cancer, and more than half of them eventually die from it. In many countries, cancer ranks as the second most common cause of death after cardiovascular disease. Early detection is associated with improved outcomes for many cancers.

[0006] Cancer is often caused by the accumulation of mutations within an individual's normal cells, at least some of which cause improperly regulated cell division. Such mutations often include single nucleotide variations (SNVs), gene fusions, insertions and deletions (indels), transversions, translocations, and inversions. The number of mutations within a cancer can be an indicator of the cancer's susceptibility to immunotherapy.

[0007] Cancer is usually detected by biopsy of the tumor, followed by analysis of cytopathology, biomarkers or DNA extracted from cells. But it has recently been proposed that cancer can also be detected according to cell-free nucleic acids (e.g., circulating nucleic acids, circulating tumor nucleic acids, exosomes, nucleic acids from apoptotic cells and / or necrotic cells) in body fluids such as blood or urine (see, e.g., Siravegna et al., Nature Reviews, 14: 531-548 (2017)). Such detection has the advantage that they are non-invasive and can be performed without identifying suspected cancer cells and identifying sample nucleic acids from all parts of the cancer by biopsy. However, such tests are complicated by the fact that the amount of nucleic acids released into body fluids is low and variable, and the amount of nucleic acids in analyzable form recovered from such liquids is also low and variable. These sources of variation can obscure the predicted value of the comparison of tumor mutational burden (TMB) between samples.

[0008] TMB is a measure of mutations in the tumor genome carried by tumor cells. TMB is a type of biomarker that can be used to evaluate whether a subject diagnosed with cancer or suspected of having evidence of cancer will benefit from a particular type of cancer treatment, such as immuno-oncology (IO) therapy. SUMMARY OF THE INVENTION

[0010] The present application discloses methods, computer-readable media and systems that can be used to determine and analyze the tumor mutation burden (TMB) in patient samples and help guide cancer treatment decisions. Traditionally, when tumor score (e.g., mutant allele score (MAF)) and / or coverage are low, the TMB obtained by calculating the mutation rate is usually inaccurate because the assay sensitivity for calling mutations is reduced. Therefore, in some aspects, the observed TMB is adjusted according to various measures of assay sensitivity, such as tumor score (which sets the MAF of mutations in a given sample), coverage, etc. For example, in the absence of such adjustment, TMB- is high, but samples with low tumor score and / or low coverage are usually reported as TMB-low by mistake. When making treatment decisions according to such results, such results may have a significant impact on patients downstream. Therefore, before implementing the adjustment methods and related aspects disclosed herein, the average mutation count in the control sample is usually dependent on the maximum MAF and coverage. After implementing these methods and related aspects, the average mutation count in the control or comparison sample is substantially independent of the maximum MAF and coverage.

[0011] From the following detailed description, other aspects and advantages of the present disclosure will become apparent to those skilled in the art, and only illustrative embodiments of the present disclosure are shown and described in the detailed description. As will be appreciated, the present disclosure can have other and different embodiments, and its several details can be modified in more than one obvious aspect, all without departing from the present disclosure. Accordingly, the drawings and description are considered to be illustrative rather than restrictive in nature.

[0012] In one aspect, the present disclosure provides a method for determining a subject's tumor mutation burden (TMB), the method comprising: (a) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from a subject; (b) determining the coverage of a tumor score and / or nucleic acid to generate sequencing parameters; (c) determining an expected mutation score and / or an expected distribution of an expected mutation score given the sequencing parameters to generate an expected result; and (d) adjusting the observed mutation counts given the expected result to generate an adjusted result, thereby determining the subject's TMB. In some embodiments of the methods and related embodiments disclosed herein, the observed mutation counts are adjusted based on the likelihood of new antigens associated with those counts. For example, depending on the haplotype of a given subject, a particular mutation or mutation group may be more neoantigenic in that particular subject than in other subjects. In some embodiments, the methods disclosed herein include determining a given subject's TMB at more than one time point, for example, to evaluate or monitor the subject's treatment course over time.

[0013] In another aspect, the present disclosure provides a method for determining a tumor mutation burden (TMB) of a subject, the method comprising: (a) providing a sample from the subject; (b) amplifying nucleic acids in the sample to generate amplified nucleic acids; (c) sequencing the amplified nucleic acids to generate sequence information; (d) determining observed mutation counts from the sequence information; (e) determining tumor fraction and / or coverage of the nucleic acids to generate sequencing parameters; (f) determining an expected mutation score and / or an expected distribution of expected mutation scores given the sequencing parameters to generate an expected result; and (g) adjusting the observed mutation counts given the expected result to generate an adjusted result, thereby determining the TMB of the subject.

[0014] In another aspect, the present disclosure provides a method for selecting one or more customized therapies to treat cancer in a subject, the method comprising: (a) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from the subject; (b) determining a tumor fraction and / or coverage of the nucleic acids to generate sequencing parameters; (c) determining an expected mutation score and / or an expected distribution of expected mutation scores given the sequencing parameters to generate an expected outcome; (d) adjusting the observed mutation count given the expected outcome to generate an adjusted outcome; and (e) comparing the adjusted outcome to one or more comparator results indexed with one or more treatments to identify one or more customized therapies for the subject.

[0015] In another aspect, the present disclosure provides a method for treating cancer in a subject, the method comprising: (a) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from the subject; (b) determining a tumor fraction and / or coverage of the nucleic acid to generate sequencing parameters; (c) determining an expected mutation score and / or an expected distribution of expected mutation scores given the sequencing parameters to generate an expected outcome; (d) adjusting the observed mutation count given the expected outcome to generate an adjusted outcome; (e) comparing the adjusted outcome to one or more comparator outcomes indexed with one or more treatments to identify one or more customized treatments for the subject; and (f) when there is a substantial match between the adjusted outcome and the comparator outcome, administering at least one identified customized treatment to the subject, thereby treating the subject's cancer.

[0016] In another aspect, the present disclosure provides a method of treating cancer in a subject, the method comprising administering one or more customized treatments to the subject, thereby treating the cancer in the subject, wherein the customized treatment has been identified by: (a) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from the subject; (b) determining a tumor fraction and / or coverage of the nucleic acids to generate sequencing parameters; (c) determining an expected mutation score and / or an expected distribution of expected mutation scores given the sequencing parameters to generate an expected outcome; (d) adjusting the observed mutation count given the expected outcome to generate an adjusted outcome; (e) comparing the adjusted outcome to one or more comparator outcomes indexed with the one or more treatments; and (f) identifying one or more customized treatments for the subject when there is a substantial match between the adjusted outcome and the comparator outcome.

[0017] In some embodiments, the observed mutation counts and / or tumor scores include many synonymous mutations, many non-synonymous mutations, and / or many non-coding mutations identified in the nucleic acid. In some embodiments, the observed mutation counts and / or tumor scores include many mutations selected from the group consisting of: single nucleotide variants (SNVs), insertions or deletions (indels), copy number variants (CNVs), fusions, transversions, translocations, frame shifts, duplications, repeat expansions, and epigenetic variants. In some embodiments, the observed mutation counts and / or tumor scores exclude driver mutations and / or non-tumor-related mutations (e.g., clonal hematopoiesis-derived mutations—CH mutations).

[0018] In some embodiments, the method includes determining an observed mutation count using pooled evidence for one or more possible mutations that are below the detection limit for a given single nucleotide variant (SNV) or a given insertion or deletion (indel).

[0019] In some embodiments, the method includes generating an expected mutation score that is an observed fraction of the actual mutation counts. In some embodiments, the observed mutation counts and / or tumor scores include a number of somatic mutations identified in the nucleic acid. In some embodiments, the observed mutation counts exclude one or more known cancer driver mutations and / or passenger mutations.

[0020] In some embodiments, the method includes comparing the sequence information to one or more reference sequences to identify observed mutation counts.

[0021] In some embodiments, the reference sequence comprises at least a subsequence of hg19 and / or hg38.

[0022] In some embodiments, the tumor fraction comprises the maximum mutant allele fraction (MAF) of all somatic mutations identified in the nucleic acid. In some embodiments, the tumor fraction is less than about 0.05%, about 0.1%, about 0.2%, about 0.5%, about 1%, about 2%, about 3%, about 4%, or about 5% of all nucleic acids in the sample.

[0023] In some embodiments, the method includes identifying a number of unique cfDNA fragments comprising a given nucleotide position in the nucleic acid to determine the coverage. In some embodiments, the method includes identifying the median number of unique cell-free DNA (cfDNA) molecules comprising a given nucleotide position in the nucleic acid to determine the coverage.

[0024] In some embodiments, at a given nucleotide position in a nucleic acid present in a sample, the coverage is between 10 and 50,000 cfDNA fragments.

[0025] In some embodiments, the expected mutation score and / or the expected distribution of expected mutation scores comprises a confidence interval of about 95% or more for the mutation score. In some embodiments, the method comprises using an upper limit of a 95% confidence interval for the expected mutation score to generate a lower limit for the observed mutation count.

[0026] In some embodiments, the method includes calculating the probability of identifying a mutation in a given MAF in the expected mutation allele fraction (MAF) distribution to determine the expected mutation fraction. In some embodiments, the method includes multiplying the expected relative MAF distribution by the tumor fraction to generate MAF. As used herein, the term "MAF" is not limited to fractions, but may also include mutation molecule counts in certain embodiments. In some embodiments, the distribution of expected MAF is calculated using the following binomial ratio confidence interval:

[0027] and

[0028]

[0029] Where f is the expected fraction of mutations called, n_true is the expected actual mutations, equal to the number of mutations observed given f, and z is the confidence level. In some embodiments, if f is less than a threshold f In some embodiments, if f is greater than a threshold value, then TMB is not determined. f , then determine the TMB.

[0030] In some embodiments, the method includes determining the expected outcome using the following equation:

[0031] Observed mutation fraction = ∑ MAF (P(called mutation|MAF)*P(mutation in MAF)),

[0032] where P is the probability and MAF is the mutant allele fraction.

[0033] In some embodiments, the expected distribution of relative MAF is obtained from one or more data sets of the control sample data set. In some embodiments, the control sample data set includes at least about 25, at least about 50, at least about 100, at least about 200, at least about 300, at least about 400, at least about 500, at least about 600, at least about 700, at least about 800, at least about 900, at least about 1,000, at least about 5,000, at least about 10,000, at least about 15,000, at least about 20,000, at least about 25,000, at least about 30,000 or more control samples. In some embodiments, the control sample used in the control sample data set can be cancer type specific and / or treatment specific.

[0034] In some embodiments, the maximum MAF observed in the control sample data set comprises about 0.5%, about 1%, about 2%, about 5%, or about 10%.

[0035] In some embodiments, the method includes fitting the relative MAF to the curve using the following equation:

[0036] F = 1 / (1+(P_50 / relative-MAF) n ),

[0037] where F is the cumulative distribution function, P_50 is the median relative MAF, Relative-MAF is the relative MAF, and n is an exponent that fits the relative distribution shape.

[0038] In some embodiments, the method includes dividing the observed mutation count by the expected mutation score in the sample or the upper / lower limit of the confidence interval of the expected mutation score to generate an adjusted result. In some embodiments, the adjusted result includes a number of mutations detected in the nucleic acid at a range of mutant allele scores. In some embodiments, the method includes dividing the observed mutation count by the expected result to produce an adjusted result. In some embodiments, the adjusted result includes a prediction of the highest possible actual mutation count. In some embodiments, the adjusted result includes a prediction of the lowest possible actual mutation count. In some embodiments, the adjusted result includes an adjusted mutation count. In some embodiments, the adjusted mutation count is greater than or equal to the observed mutation count. In some embodiments, the adjusted mutation count / adjusted result is divided by the product of the size of the target genomic region analyzed and the exome calibration factor to determine the TMB score. In certain embodiments, the exome calibration factor is at least 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.10 or higher, while in other embodiments, the exome calibration factor comprises a value less than 1.0 (e.g., about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 or 0.9). In some embodiments, the exome calibration factor is determined from the exon mutation rate of the sample in the cancer database (e.g., TCGA, etc.). In certain embodiments, the exome calibration factor and exome mutation rate specific to the given cancer type under consideration are determined from samples having that cancer type.

[0039] In some embodiments, the method further comprises classifying the sample as a TMB-high sample. In some embodiments, if the TMB score of the sample is greater than a threshold TMB 评分 , then the sample is classified as TMB-high. If the TMB score of the sample is below the threshold TMB 评分 , then the sample is classified as a TMB-low sample. In certain embodiments, for example, the threshold TMB 评分 It may be about 5, 10, 15, 20, 25, 30, 35, 40 or another selected threshold.

[0040] In some embodiments, the method includes obtaining sequence information from a nucleic acid in a sample from a subject. In some embodiments, the sequence information is obtained from a targeted segment of the nucleic acid. In some embodiments, the targeted segment comprises between about 1 and about 100,000 different and / or overlapping genomic regions.

[0041] In some embodiments, the method includes obtaining a sample from a subject. In some embodiments, the sample is selected from the group consisting of: tissue, blood, plasma, serum, sputum, urine, semen, vaginal fluid, feces, synovial fluid, spinal fluid and saliva. In some embodiments, the sample includes tissue. In some embodiments, the sample includes blood, plasma and / or serum. In some embodiments, the subject is a mammalian subject. In some embodiments, the mammalian subject is a human subject. In some embodiments, the nucleic acid includes cell-free nucleic acid. In some embodiments, the nucleic acid includes cellular nucleic acid. In some embodiments, the nucleic acid includes circulating tumor nucleic acid. In some embodiments, the nucleic acid is obtained from circulating tumor cells. In some embodiments, the nucleic acid includes deoxyribonucleic acid (DNA) and / or ribonucleic acid (RNA).

[0042] In some embodiments, the method includes at least one section of a nucleic acid in an amplified sample to generate at least one amplified nucleic acid. In some embodiments, the method includes sequencing the amplified nucleic acid to generate sequence information. In some embodiments, the method includes sequencing at least about 50,000, about 100,000, about 150,000, about 200,000, about 250,000, about 500,000, about 750,000, about 1,000,000, about 1,500,000, about 2,000,000 or more nucleotides of the nucleic acid to generate sequence information. In some embodiments, sequencing is selected from the group consisting of: targeted sequencing, intron sequencing, exome sequencing, and whole genome sequencing.

[0043] In some embodiments, the customized treatment includes at least one immunotherapy. In some embodiments, the immunotherapy includes at least one checkpoint inhibitor antibody. In some embodiments, the immunotherapy includes antibodies against: PD-1, PD-2, PD-L1, PD-L2, CTLA-40, OX40, B7.1, B7He, LAG3, CD137, KIR, CCR5, CD27 or CD40. In some embodiments, the immunotherapy includes administering proinflammatory cytokines against at least one tumor type. In some embodiments, the immunotherapy includes administering T cells against at least one tumor type.

[0044] In some embodiments, the cancer comprises at least one tumor type selected from the group consisting of biliary tract cancer, bladder cancer, transitional cell carcinoma, urothelial carcinoma, brain cancer, glioma, astrocytoma, breast cancer, metaplastic carcinoma, cervical cancer, cervical squamous cell carcinoma, rectal cancer, colorectal cancer, colon cancer, hereditary nonpolyposis colorectal cancer, colorectal adenocarcinoma, gastrointestinal stromal tumor (GIST), endometrial cancer, endometrial stromal sarcoma, esophageal cancer, esophageal squamous cell carcinoma, esophageal adenocarcinoma, ocular melanoma, uveal melanoma, gallbladder cancer, gallbladder adenocarcinoma, renal cell carcinoma, clear cell renal cell carcinoma, transitional cell carcinoma, urothelial carcinoma, Wilms tumor, leukemia, acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia disease (CML), chronic myelomonocytic leukemia (CMML), liver cancer, hepatoepithelial carcinoma, hepatocellular carcinoma, cholangiocarcinoma, hepatoblastoma, lung cancer, non-small cell lung cancer (NSCLC), mesothelioma, B-cell lymphoma, non-Hodgkin's lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, T-cell lymphoma, non-Hodgkin's lymphoma, precursor T-lymphoblastic lymphoma / leukemia, peripheral T-cell lymphoma, multiple myeloma, nasopharyngeal carcinoma (NPC), neuroblastoma, oral cancer, oral squamous cell carcinoma, osteosarcoma, ovarian cancer, pancreatic cancer, pancreatic ductal adenocarcinoma, pseudopapillary tumor, acinar cell carcinoma, prostate cancer, prostate adenocarcinoma, skin cancer, melanoma, malignant melanoma, cutaneous melanoma, small intestine cancer, gastric cancer, gastric epithelial carcinoma, gastrointestinal stromal tumor (GIST), uterine cancer and uterine sarcoma.

[0045] In some embodiments, the sequence information includes sequence reads of nucleic acids generated by a nucleic acid sequencer. In some embodiments, the nucleic acid sequencer performs pyrophosphate sequencing, single molecule sequencing, nanopore sequencing, semiconductor sequencing, synthetic sequencing, ligation sequencing, or hybridization sequencing on the nucleic acid to generate sequencing reads.

[0046] In some embodiments, the method further comprises selectively enriching one or more regions from the genome or transcriptome prior to sequencing. In some embodiments, the method further comprises amplifying the selectively enriched region prior to sequencing. In some embodiments, the sequence information is obtained from a targeted segment of a nucleic acid, wherein the targeted segment is obtained by selectively enriching one or more regions from the genome or transcriptome prior to sequencing. In some embodiments, the method further comprises amplifying the obtained targeted segment prior to sequencing.

[0047] In some embodiments, the method further comprises attaching one or more adapters comprising molecular barcodes to the nucleic acid before sequencing. In some embodiments, the nucleic acid is uniquely barcoded. In some embodiments, the nucleic acid is non-uniquely barcoded. In some embodiments, the adapter comprises between 2 and 1,000,000 molecular barcodes. In some embodiments, the adapter comprises between 2 and 100 molecular barcodes. In some embodiments, the adapter comprises between 2 and 200 molecular barcodes. In some embodiments, the adapter comprises between 2 and 100 molecular barcodes. In some embodiments, the method comprises randomly attaching adapters comprising molecular barcodes to each end of the nucleic acid. In some embodiments, the adapter is attached to the nucleic acid by blunt end connection or sticky end connection. In some embodiments, the adapter is a T-tail and / or C-tail adapter.

[0048] In some embodiments, the method further comprises grouping the sequence reads into sequence read families, each family comprising sequence reads generated from nucleic acids in the sample.

[0049] In some embodiments, at least a portion of the method is computer-implemented.In some embodiments, the method further comprises generating a report in an electronic format providing one or more TMB scores.

[0050] In another aspect, the present disclosure provides a system comprising a controller comprising or having access to a computer-readable medium comprising non-transitory computer-executable instructions which, when executed by at least one electronic processor, at least perform: (i) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from a subject; (ii) determining a tumor fraction and / or coverage of the nucleic acids to generate sequencing parameters; (iii) determining an expected mutation fraction and / or an expected distribution of expected mutation fractions given the sequencing parameters to generate an expected outcome; and (iv) adjusting the observed mutation count given the expected outcome to generate an adjusted outcome, thereby determining a tumor mutation burden (TMB) for the subject.

[0051] In some embodiments, the system includes a nucleic acid sequencer operably connected to a controller, the nucleic acid sequencer being configured to provide sequence information of nucleic acids in a sample from a subject. In some embodiments, the nucleic acid sequencer is configured to perform pyrophosphate sequencing, single molecule sequencing, nanopore sequencing, semiconductor sequencing, synthetic sequencing, ligation sequencing, or hybridization sequencing on the nucleic acid to generate sequencing reads. In some embodiments, the nucleic acid sequencer or another system component is configured to group the sequence reads generated by the nucleic acid sequencer into sequence read families, each family including sequence reads generated from nucleic acids in a sample.

[0052] In some embodiments, the system includes a database operably connected to the controller, the database including one or more comparator results indexed with one or more treatments, and wherein the electronic processor further performs at least: (v) comparing the adjusted result to the one or more comparator results, wherein a substantial match between the adjusted result and the comparator result indicates a predicted response of the subject to the treatment.

[0053] In some embodiments, the system includes a sample preparation component operably connected to a controller, the sample preparation component being configured to prepare nucleic acids in a sample for sequencing by a nucleic acid sequencer. In some embodiments, the sample preparation component is configured to selectively enrich a region from a genome or transcriptome in a sample. In some embodiments, the sample preparation component is configured to attach one or more adapters comprising a molecular barcode to a nucleic acid.

[0054] In some embodiments, the system includes a nucleic acid amplification component operably connected to a controller, the nucleic acid amplification component being configured to amplify nucleic acids in a sample from a subject. In some embodiments, the nucleic acid amplification component is configured to amplify a region selectively enriched from a genome or transcriptome in a sample.

[0055] In some embodiments, the system includes a material transfer component operably connected to the controller, the material transfer component configured to transfer one or more materials between at least the nucleic acid sequencer and the sample preparation component.

[0056] In another aspect, the present disclosure provides a computer-readable medium comprising non-transitory computer-executable instructions, which, when executed by at least one electronic processor, at least perform: (i) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from a subject; (ii) determining a tumor score and / or a coverage of the nucleic acid to generate sequencing parameters; (iii) determining an expected mutation score and / or an expected distribution of expected mutation scores given the sequencing parameters to generate an expected result; and (iv) adjusting the observed mutation count given the expected result to generate an adjusted result, thereby determining a tumor mutation burden (TMB) of the subject. In some embodiments, the adjusted mutation count is determined using the expected mutation score.

[0057] The method implemented using the system disclosed herein and computer readable medium includes many different embodiments. In some embodiments, for example, the observed mutation count and / or tumor score include many synonymous mutations, many non-synonymous mutations and / or many non-coding mutations identified in nucleic acid. In certain embodiments, the observed mutation count and / or tumor score include many mutations selected from the group consisting of: single nucleotide variants (SNV), insertions or deletions (indel), copy number variants (CNV), fusions, transversions, translocations, frameshifts, repetitions, repeat amplifications and epigenetic variants. In other exemplary embodiments, the observed mutation count and / or tumor score excludes driver mutations and / or non-tumor related mutations (e.g., clonal hematopoietic derived mutations). Optionally, the observed mutation count is determined using one or more possible mutations that are less than the detection limit of a given single nucleotide variant (SNV) or a given insertion or deletion (indel) The collected evidence of the limit of detection is used to determine the observed mutation count.

[0058] In some embodiments, the mutation count adjusted is determined using the expected mutation score. Typically, the observed mutation count and / or tumor score includes many somatic mutations identified in the nucleic acid. In some of these embodiments, the observed mutation count excludes one or more known cancer driver mutations and / or passenger mutations. In certain embodiments, the observed mutation count is determined by comparing the sequence information with one or more reference sequences (e.g., at least a subsequence of hg19, hg38, etc.).

[0059] In certain embodiments, the tumor score includes the maximum mutant allele fraction (MAF) of all somatic mutations identified in the nucleic acid. Typically, the tumor score is less than about 0.05%, about 0.1%, about 0.2%, about 0.5%, about 1%, about 2%, about 3%, about 4%, or about 5% of all nucleic acids in the sample. In some embodiments, coverage is determined by identifying the median number of unique cell-free DNA (cfDNA) molecules containing a given nucleotide position in the nucleic acid. In some embodiments, for example, at a given nucleotide position in a nucleic acid present in a sample, coverage is between 10 and 50,000 cfDNA fragments.

[0060] In some embodiments, the expected mutation score and / or the expected distribution of the expected mutation score include about 95% or more confidence intervals of the mutation score. In certain embodiments, the upper limit of the 95% confidence interval of the expected mutation score is used to generate the lower limit of the observed mutation count. In some embodiments, the expected mutation score is determined by calculating the probability of identifying a mutation in a given mutant allele fraction (MAF) in the expected MAF distribution. Optionally, MAF is generated by multiplying the distribution of the relative MAF by the tumor score. In some embodiments, the distribution of the expected MAF is calculated using the following binomial ratio confidence interval:

[0061] and

[0062]

[0063] Where f is the expected fraction of mutations called, n_true is the expected actual mutations, equal to the number of mutations observed given f, and z is the confidence level. Certain embodiments of the systems or computer-readable media disclosed herein include using the following equation to determine the expected outcome:

[0064] Observed mutation fraction = ∑ MAF (P(called mutation|MAF)*P(mutation at MAF)), where P is the probability and MAF is the mutant allele fraction.

[0065] In certain embodiments, the expected distribution of MAF is obtained from the relative MAF observed in at least one control sample data set. In some embodiments, the comparator result includes at least about 25, at least about 50, at least about 100, at least about 200, at least about 300, at least about 400, at least about 500, at least about 600, at least about 700, at least about 800, at least about 900, at least about 1,000, at least about 5,000, at least about 10,000, at least about 15,000, at least about 20,000, at least about 25,000, at least about 30,000 or more control samples. In certain embodiments, the control sample in the control sample data set includes about 0.5%, about 1%, about 2%, about 5% or about 10% of maximum MAF. In some embodiments of system disclosed herein or computer readable medium, include using the following equation to fit relative MAF to curve:

[0066] F = 1 / (1+(P_50 / relative-MAF) n ),

[0067] Wherein F is cumulative distribution function, P_50 is relative MAF median, relative-MAF is relative MAF, and n is the index of fitting relative distribution shape.In some embodiments, the result of adjustment is generated by dividing the observed mutation count by the upper limit / lower limit of the confidence interval of the expected mutation score or expected mutation score in the sample.In certain embodiments, the result of adjustment includes many mutations detected in nucleic acid by a series of mutation allele scores.In some embodiments, the result of adjustment is generated by dividing the observed mutation count by the expected result.In certain embodiments, the result of adjustment includes the prediction of the highest possible actual mutation count.In some embodiments, the result of adjustment includes the prediction of the lowest possible actual mutation count.In certain embodiments, the result of adjustment includes the mutation count adjusted.In some of these embodiments, the mutation count adjusted is greater than or equal to the mutation count observed.In some embodiments, the mutation count adjusted / the result of adjustment is divided by the product of the size of the target genomic region analyzed and the exon group calibration factor, to determine the TMB score. In certain embodiments, the exome calibration factor is at least 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.10 or a higher value, while in other embodiments, the exome calibration factor includes a value less than 1.0 (e.g., about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 or 0.9). In some embodiments, the exome calibration factor is determined from the exon mutation rate of the sample in the cancer database (e.g., TCGA, etc.). In certain embodiments, the exome calibration factor and exome mutation rate specific to the given cancer type under consideration are determined from samples having the cancer type. In some embodiments, if the TMB score of a given sample is greater than a threshold TMB 评分 , then the sample is usually classified as a TMB-high sample. In contrast, when the TMB score of the sample is below the threshold TMB 评分 In some embodiments, the threshold TMB 评分 It may be, for example, about 5, 10, 15, 20, 25, 30, 35, 40, or another selected threshold.

[0068] On the other hand, the present disclosure provides a system comprising a communication interface that obtains sequencing information of one or more nucleic acids in a sample from a subject through a communication network; and a computer that communicates with the communication interface, wherein the computer comprises at least one computer processor and a computer-readable medium comprising machine executable code, wherein the machine executable code, when executed by at least one computer processor, implements a method comprising: (i) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from a subject; (ii) determining a tumor score and / or coverage of the nucleic acid to generate sequencing parameters; (iii) determining an expected mutation score and / or an expected distribution of expected mutation scores given the sequencing parameters to generate an expected result; and (iv) adjusting the observed mutation count given the expected result to generate an adjusted result, thereby detecting a tumor mutation burden (TMB) of the subject.

[0069] In some embodiments, the sequencing information is provided by a nucleic acid sequencer. In some embodiments, the nucleic acid sequencer performs pyrophosphate sequencing, single molecule sequencing, nanopore sequencing, semiconductor sequencing, synthetic sequencing, ligation sequencing, or hybridization sequencing on the nucleic acid to generate sequencing reads. In some embodiments, the nucleic acid sequencer generates sequencing reads using a clonal single molecule array derived from a sequencing library. In some embodiments, the nucleic acid sequencer includes a chip having a micropore array for sequencing a sequencing library to generate sequencing reads.

[0070] In some embodiments, the computer readable medium comprises a memory, a hard drive, or a computer server. In some embodiments, the communication network comprises one or more computer servers capable of distributed computing. In some embodiments, distributed computing is cloud computing. In some embodiments, the computer is located on a computer server, and the computer server is located at a remote location of the nucleic acid sequencer.

[0071] In some embodiments, the computer-readable medium further comprises: an electronic display that communicates with a computer via a network, wherein the electronic display comprises a user interface for displaying the result after implementing (i)-(iv). In some embodiments, the user interface is a graphical user interface (GUI) or a network-based user interface. In some embodiments, the electronic display is in a personal computer. In some embodiments, the electronic display is in a computer that supports the Internet. In some embodiments, the computer that supports the Internet is located away from the computer. In some embodiments, the computer-readable medium comprises a memory, a hard drive, or a computer server. In some embodiments, the communication network comprises a telecommunications network, the Internet, an extranet, or an intranet.

[0072] In some embodiments, the observed mutation counts and / or tumor fractions do not include somatic mutations that are less than about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 15%, about 20%, about 25%, or about 30% of the maximum MAF. In some embodiments, the expected / adjusted mutation counts are adjusted using an exome calibration factor. In some embodiments, the adjusted mutation counts are divided by the exome calibration factor.

[0073] In some embodiments, mutation counts are determined in a set of genes or genomic regions comprising the genes listed in Table 2.

[0074] In another aspect, the present disclosure provides a method for characterizing a cell-free nucleic acid molecule sample from a subject having or suspected of having cancer, comprising assaying the sample to determine whether there is a genetic variation in at least 100, 200, 300, 400, or 500 genes or genomic regions selected from those listed in Table 2. In some embodiments, the method is performed on no more than 1,000 genes. In some embodiments, the method further comprises administering a cancer treatment to the subject, the cancer treatment being determined by the presence of a genetic variation in at least one gene determined in Table 2. In some embodiments, the method further comprises isolating nucleic acid molecules from the sample and enriching nucleic acid molecules corresponding to at least 100, 200, 300, 400, or 500 genes with probes containing segments from at least 100, 200, 300, 400, or 500 genes.

[0075] In yet another aspect, the present disclosure provides a method for analyzing a cell-free DNA sample from a subject having or suspected of having cancer, comprising selectively enriching at least 100, 200, 300, 400, or 500 genomic regions from the group consisting of the genes listed in Table 2 to produce an enriched library; amplifying the enriched library and performing a sequencing reaction; and analyzing the presence of genetic variants in the genomic regions.

[0076] Method disclosed herein and related systems and computer readable medium implementation include various embodiments.These methods and related aspects generally include generating TMB scores for samples.In some applications, subclonality filters (subclonality filter) are used.In some of these embodiments, observed mutation counts and / or tumor scores exclude somatic mutations by filtering out somatic mutations with low MAF.In some embodiments, for example, subclonality filters are used to exclude somatic mutations less than about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 15%, about 20%, about 25% or about 30% of maximum MAF.In some embodiments, instead of being excluded / filtered out, somatic mutations are weighted (that is, each mutation obtains a certain weight, rather than just low MAF mutations) based on clonality.In other exemplary embodiments, expected and / or adjusted mutation counts are adjusted using exon group calibration factors.In some of these embodiments, the adjusted mutation counts are divided by exon group calibration factors. In certain embodiments, the exome calibration factor is at least 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.10 or higher, while in other embodiments, the exome calibration factor comprises a value less than 1.0 (e.g., about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 or 0.9). In some embodiments, the exome calibration factor is determined from the exon mutation rate of the sample in the cancer database (e.g., TCGA, etc.). In certain embodiments, the exome calibration factor and exome mutation rate specific to the given cancer type under consideration are determined from samples having that cancer type.

[0077] In some embodiments, an alternative method other than using the maximum MAF is used to estimate the tumor score. In some embodiments, for example, the coverage of a predetermined set of genomic regions and the standard maximum likelihood method are used to estimate the tumor score. In certain embodiments, the difference in the cfDNA fragment size distribution of the region with copy number changes in the predetermined set of genomic regions and the sample is used to determine the tumor score. Optionally, the MAF of germline and / or somatic variants is adjusted to the copy number changes observed in the sample to estimate the tumor score. Typically, germline variants are excluded from the determined TMB score. In some embodiments, germline / somatic state is determined using a β-binomial distribution model, which estimates the mean and variance of the MAF of the common germline SNPs (common germline SNPs) located near the candidate variant under consideration. Optionally applicable to the β-binomial distribution model for realizing the methods disclosed herein and related aspects, the other details related to the description are described in, for example, [PCT / US2018 / 052087 submitted on September 20, 2018], which is incorporated by reference. In addition, coverage is also determined using various methods. In some embodiments, coverage is incorporated as the median of unique cfDNA fragments for each base in a given sample. In some of these embodiments, for example, the model can be made base-specific, and the method includes using the number of unique cfDNA fragments at each base to calculate the sensitivity at the base. Typically, at a given base position, coverage can be at least 10, 50, 100, 500, 1000, 5000, 10,000, 20,000, 50,000 or more cfDNA fragments.

[0078] In some embodiments, the count of somatic mutations (observed mutation counts) does not include (i) driver mutations that do not represent the background exome mutation rate or TMB, etc., (ii) mutations that may come from clonal hematopoiesis rather than the tumor under consideration, and / or (iii) resistance mutations. In some embodiments, certain types of somatic mutations are weighted rather than excluded or filtered out from the observed mutation counts. In certain embodiments, clonal hematopoietic mutations are identified by: (i) using a curated list of mutations frequently observed in blood-related cancers based on the literature and cancer databases (e.g., COSMIC, etc.), (ii) using their context in the sample (e.g., MAF) (e.g., the presence of other clonal hematopoietic variants at similar MAF or within a similar MAF range) and / or by analyzing clonal hematopoietic mutations in a previously studied sample database, and / or (iii) by sequencing DNA in a patient sample derived from blood (e.g., white blood cells). Clonal hematopoiesis (e.g., "clonal hematopoiesis of uncertain potential" or "CHIP") mutations can also be identified based on other factors, such as patient age, methylation status, enrichment of mutations in certain populations, methods for estimating tumor fractions also disclosed herein, and the like. In certain embodiments, resistance mutations can be identified using, for example, a curated list of mutations frequently observed in patient samples based on literature and cancer databases, and / or by analyzing a database of previously studied samples. Resistance mechanisms or any other process may introduce a large number of mutations or clusters of mutations in a gene (e.g., KRAS in colorectal cancer or BRCA1 / 2 reversal in prostate cancer treated with PARP inhibitors). In some embodiments, the observed mutation count excludes at least some mutations in a particular gene, for example, if the number of mutations in the particular gene is significantly greater than the expected number of mutations in the gene based on the total sample mutation count and the gene size in a given group. In certain embodiments, the count of somatic mutations also includes SNVs and indels at sites that are not reported as part of the particular group used.

[0079] TMB scores are often used to predict whether a patient will respond to immunotherapy. For example, a specific type of mutation in certain genes (e.g., loss of function of STK11, KEAP1, PTEN, etc.) and / or the presence of a patient's mutation signature in a group of genes analyzed (e.g., a patient has a C>T transition, etc.) can be used in conjunction with TMB scores to predict possible responses to treatment. Typically, loss of function of gene / driver mutations takes precedence, and the patient will not respond to treatment. That is, when loss of function of certain genes (such as but not limited to STK11, KEAP1, and PTEN) is observed, the patient may not respond to immunotherapy regardless of the TMB score.

[0080] In some embodiments, certain factors, such as mutations in specific genes (e.g., DNA repair system genes—MLH1, MLH2 & MLH3, MSH2 & MSH3, MSH6, PMS1 & PMS2; polymerase E) and the methylation status of certain regions specific to the promoter regions of DNA repair system genes can be used in combination with the TMB score to classify samples as TMB-high samples or TMB-low samples.

[0081] Depending on the specific circumstances, such as TMB score, cancer type and / or staging, etc., substantially any treatment or treatment combination is optionally applied to a given patient. Examples of such treatments include immunotherapy, such as CAR-T cell therapy, vaccines (e.g., universal or patient-specific neoantigens), oligonucleotides or vector-based genetic therapy, distant effect intervention (e.g., radiotherapy), immunotherapy treatment / drugs (e.g., anti-TIGIT), immune checkpoint inhibitors and / or antibodies (e.g., anti-TIGIT; antibodies for PD-1, PD-2, PD-L1, PD-L2, CTLA-40, OX40, B7.1, B7He, LAG3, CD137, KIR, CCR5, CD27 or CD40) and / or combined therapy (e.g., immunotherapy+PARPi+chemotherapy, etc.), as well as many other treatments further illustrated herein or otherwise known to those of ordinary skill in the art.

[0082] TMB determination as described herein is also optionally combined with other evaluations or techniques to further inform treatment decisions. Some examples of these include assessing the mechanical defects of cells (e.g., lack of response to a given treatment), evaluating increased aneuploidy (e.g., to measure the response to the potential reduction of IO treatment), also determining other non-TMB features of patient status (e.g., age, haplotype, race, sex, etc.), determining that TMB scores are combined with human leukocyte antigen (HLA) deletions, HLA sequencing (e.g., as a mechanism for new antigen prediction), transcriptomics, immune repertoire sequencing and / or other analytical methods to predict the lack of a therapeutic response or the possibility of such a response.

[0083] In some embodiments, the results of the systems and methods disclosed herein are used as input to generate a report. The report can be in paper or electronic format. For example, the results of the adjustments obtained by the methods and systems disclosed herein can be directly displayed in such a report. Alternatively or additionally, diagnostic information or treatment recommendations based on the results of the adjustments can be included in the report.

[0084] More than one step of a method disclosed herein, or steps performed by a system disclosed herein, can be performed at the same or different times, in the same or different geographic locations such as countries, and / or by the same or different persons.

[0085] In another aspect, the present disclosure provides a method for classifying a subject as a candidate for immunotherapy, the method comprising: (a) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from the subject; (b) determining a tumor fraction and / or a coverage of the nucleic acid to generate sequencing parameters; (c) determining an expected mutation score and / or an expected distribution of expected mutation scores given the sequencing parameters to generate an expected outcome; (d) adjusting the observed mutation count given the expected outcome to generate an adjusted outcome; and (e) determining a TMB score using the adjusted outcome, and (f) comparing the TMB score to a threshold TMB score. 评分 The comparison is performed to categorize the subjects as candidates for immunotherapy.

[0086] On the other hand, the present disclosure relates to a method for generating neoantigen-orphan immune receptor information at least in part using a computer (i.e., using a computer to perform some or all steps). The method includes (a) receiving, by a computer, sequence information obtained from nucleic acids in a blood sample obtained from a subject diagnosed with cancer, wherein at least a first portion of the sequence information includes sequencing reads obtained from cell-free nucleic acids (cfNA) in the blood sample, and wherein at least a second portion of the sequence information includes sequencing reads obtained from nucleic acids derived from one or more immune cells in the blood sample. The method also includes (b) determining a tumor mutation burden (TMB) score for the subject from at least a first portion of the sequence information, and (c) identifying one or more clonotypes of an immune repertoire in at least a second portion of the sequence information. In addition, the method also includes (d) associating the TMB score with one or more clonotypes to identify one or more neoantigen-orphan immune receptors in the subject, thereby generating neoantigen-orphan immune receptor information.

[0087] In some embodiments, the method also includes identifying one or more customized treatments for the subject using neoantigen-orphan immune receptor information. In some of these embodiments, the method also includes applying one or more customized treatments to the subject. In some embodiments, the method includes identifying one or more variants in the first part of the sequence information to determine the TMB score, wherein the variant includes one or more mutations selected from the group consisting of: single nucleotide variants (SNV), insertions or deletions, copy number variants (CNV), fusions, transversions, translocations, frameshifts, duplications, repeat variants and epigenetic variants. In some of these embodiments, repeat variants include one or more microsatellite variants. In some of these embodiments, the method also includes identifying one or more other variants in the second part of the sequence information to determine the TMB score, wherein the other variants include one or more somatic mutations in the nucleic acid derived from one or more immune cells in a blood sample.

[0088] On the other hand, the present disclosure relates to a method for analyzing more than one analyte in a blood sample from a subject diagnosed with cancer. The method includes: (a) separating a first set of cell-free nucleic acids (cfNA) in the blood sample and a second set of nucleic acids from one or more immune cells in the blood sample, and (b) amplifying one or more regions encoding at least a portion of the α and / or β subunits of the T cell receptor in the second set of nucleic acids to produce a second set of enriched nucleic acids. The method also includes (c) sequencing one or more regions of the first set of cfNA and one or more regions of the second set of enriched nucleic acids to generate sequence information, and (d) determining a tumor mutation burden (TMB) score for the subject based on the sequence information. In addition, the method also includes (e) identifying one or more clonotypes of the immune spectrum library based on the sequence information, and (f) associating the TMB score with the one or more clonotypes to identify one or more new antigens-orphan immune receptors in the subject, thereby analyzing more than one analyte in a blood sample from a subject diagnosed with cancer.

[0089] In another aspect, the present disclosure relates to a method for analyzing nucleic acids in a blood sample from a subject using, at least in part, a computer. The method includes (a) receiving, by a computer, sequence information obtained from nucleic acids in a blood sample from a subject, wherein at least a first portion of the sequence information includes sequencing reads obtained from cell-free nucleic acids (cfNA) in the blood sample, and wherein at least a second portion of the sequence information includes sequencing reads obtained from nucleic acids derived from one or more immune cells in the blood sample. The method also includes (b) identifying one or more variants in the first portion of the sequence information and one or more clonotypes of an immune spectral library in the second portion of the sequence information, thereby analyzing nucleic acids in a blood sample from a subject.

[0090] In some embodiments, the method includes (i) determining an observed mutation count in a first portion of the sequence information, (ii) determining a tumor fraction and / or coverage of at least cfNA in the first portion of the sequence information to generate sequencing parameters, (iii) determining an expected mutation score and / or an expected distribution of expected mutation scores given the sequencing parameters to generate an expected outcome, and (iv) adjusting the observed mutation count given the expected outcome to generate an adjusted outcome, thereby determining a tumor mutation burden (TMB) for the subject. In certain embodiments, the method includes (i) quantifying a number of different repeat lengths present in each of more than one repetitive nucleic acid site from the first portion of the sequence information and / or the second portion of the sequence information to generate a site score for each of the more than one repetitive nucleic acid site, wherein the sequence information includes a population of repetitive nucleic acid sites, (ii) calling a given repetitive nucleic acid site as unstable when the site score of the given repetitive nucleic acid site exceeds a site-specific training threshold for the given repetitive nucleic acid site to generate a repetitive nucleic acid instability score including many unstable repetitive nucleic acid sites from more than one repetitive nucleic acid site, and (iii) classifying the repetitive nucleic acid instability state of the blood sample as unstable when the repetitive nucleic acid instability score exceeds a population training threshold for a population of repetitive nucleic acid sites in the blood sample, thereby determining the repetitive nucleic acid instability state of the blood sample.

[0091] In certain embodiments, the method includes (i) quantifying a number of different repeat lengths present in each of more than one repetitive deoxyribonucleic acid (DNA) sites from a first portion of the sequence information and / or a second portion of the sequence information to generate a site score for each of the more than one repetitive DNA sites, wherein the sequence information includes a population of repetitive DNA sites, (ii) comparing the site score of a given repetitive DNA site to a site-specific training threshold for the given repetitive DNA site for each of the more than one repetitive DNA sites, (iii) calling the given repetitive DNA site as unstable when the site score of the given repetitive DNA site exceeds the site-specific training threshold for the given repetitive DNA site to generate a repetitive DNA instability score including a number of unstable repetitive DNA sites from more than one repetitive DNA sites, and (iv) classifying the repetitive DNA instability state of the blood sample as unstable when the repetitive DNA instability score exceeds the population training threshold for the population of repetitive DNA sites in the blood sample, thereby determining the repetitive DNA instability state of the blood sample. In some embodiments, the method includes (i) quantifying a number of different repeat lengths present at each of more than one microsatellite loci from the first portion of the sequence information and / or the second portion of the sequence information to generate a locus score for each of the more than one microsatellite loci, wherein the sequence information includes a population of microsatellite loci, (ii) calling a given repetitive nucleic acid locus as unstable when the locus score for the given repetitive nucleic acid locus exceeds a locus-specific training threshold for the given repetitive nucleic acid locus to generate a repetitive nucleic acid instability score comprising a number of unstable repetitive nucleic acid loci from more than one repetitive nucleic acid loci, and (iii) classifying the repetitive nucleic acid instability state of the blood sample as unstable when the repetitive nucleic acid instability score exceeds a population training threshold for a population of repetitive nucleic acid loci in the blood sample, thereby determining the repetitive nucleic acid instability state of the blood sample.

[0092] In some embodiments, the method includes (i) quantifying a number of different repeat lengths present in each of more than one repetitive deoxyribonucleic acid (DNA) sites from a first portion of the sequence information and / or a second portion of the sequence information to generate a site score for each of the more than one repetitive DNA sites, wherein the sequence information includes a population of repetitive DNA sites, (ii) comparing the site score of a given repetitive DNA site to a site-specific training threshold for the given repetitive DNA site for each of the more than one repetitive DNA sites, (iii) calling the given repetitive DNA site as unstable when the site score of the given repetitive DNA site exceeds the site-specific training threshold for the given repetitive DNA site to generate a repetitive DNA instability score including a number of unstable repetitive DNA sites from the more than one repetitive DNA sites, and (iv) classifying the repetitive DNA instability state of the blood sample as unstable when the repetitive DNA instability score exceeds the population training threshold for the population of repetitive DNA sites in the blood sample, thereby determining the repetitive DNA instability state of the blood sample. In another embodiment, the method includes (i) quantifying a plurality of different repeat lengths present at each of more than one microsatellite loci from the first portion of the sequence information and / or the second portion of the sequence information to generate a locus score for each of the more than one microsatellite loci, wherein the sequence information includes a population of microsatellite loci, (ii) for each of the more than one microsatellite loci, comparing the locus score for a given microsatellite locus with a locus-specific training threshold for the given microsatellite locus, (iii) when the locus score for the given microsatellite locus exceeds the locus-specific training threshold for the given microsatellite locus, calling the given microsatellite locus as unstable to generate a microsatellite instability score comprising a plurality of unstable microsatellite loci from more than one microsatellite loci, and (iv) when the microsatellite instability score exceeds the population training threshold for the population of microsatellite loci in the blood sample, classifying the microsatellite instability (MSI) status of the blood sample as unstable, thereby determining the MSI status of the blood sample.

[0093] In some embodiments, the method includes capturing multiple groups of target regions of cell-free DNA (cfDNA), wherein the multiple groups of target regions include sequence variable target region groups and epigenetic target region groups, thereby generating captured cfDNA molecule groups, wherein the cfDNA molecules corresponding to the sequence variable target region groups are captured in the captured cfDNA molecule groups at a higher capture yield than the cfDNA molecules corresponding to the epigenetic target region groups. In certain embodiments, cfNA includes cell-free DNA (cfDNA). In some embodiments, nucleic acids derived from one or more immune cells in a blood sample include mRNA and / or gDNA. In some embodiments, nucleic acids derived from one or more immune cells in a blood sample encode at least a portion of an immune polypeptide selected from the group consisting of: antibodies, B cell receptors, and T cell receptors. In some embodiments, the method includes obtaining cfNA from a plasma or serum fraction of a blood sample. In certain embodiments, the method includes obtaining nucleic acids derived from one or more immune cells from a buffy coat fraction of a blood sample.

[0094] In another aspect, the present disclosure relates to a system comprising a communication interface that obtains sequencing information of one or more nucleic acids in a sample from a subject through a communication network. The system also includes a computer in communication with the communication interface, wherein the computer includes at least one computer processor and a computer-readable medium containing machine executable code, which implements a method when executed by at least one computer processor, the method comprising: (i) receiving sequence information obtained from nucleic acids in a blood sample obtained from a subject diagnosed with cancer, wherein at least a first portion of the sequence information includes sequencing reads obtained from cell-free nucleic acids (cfNA) in the blood sample, and wherein at least a second portion of the sequence information includes sequencing reads obtained from nucleic acids derived from one or more immune cells in the blood sample, (ii) determining a tumor mutation burden (TMB) score for the subject based on at least the first portion of the sequence information, (iii) identifying one or more clonotypes of an immune repertoire in at least the second portion of the sequence information, and (iv) associating the TMB score with the one or more clonotypes to identify one or more neoantigens-orphan immune receptors in the subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate certain embodiments and are used together with the written description to explain certain principles of the methods, computer-readable media, and systems disclosed herein. The description provided herein can be better understood when read in conjunction with the accompanying drawings, which are included by way of example and not by way of limitation. It should be understood that, unless the context otherwise indicates, the same reference numerals represent the same components in all the drawings. It should also be understood that some or all of the drawings may be schematic representations for illustrative purposes and do not necessarily depict the actual relative sizes or positions of the elements shown.

[0097] Figure 1 is a flow chart schematically depicting exemplary method steps for adjusting TMB according to some embodiments of the present invention.

[0098] Figure 2 is a schematic diagram of an exemplary system suitable for use with certain embodiments of the present invention.

[0099] Figure 3 is a flow chart showing a combined workflow of cell-free DNA analysis and immune repertoire sequencing from the same test sample according to one embodiment of the present disclosure. The integrated results from immune repertoire profiling and TMB analysis provide an enhanced immunotherapy response score.

[0100] Figure 4A and Figure 4B is a schematic diagram showing a method for TCR gDNA assay ( Figure 4A ) and immune receptor RNA assays ( Figure 4B ) sample preparation method.

[0101] Figure 5 is a flow chart illustrating a combined workflow of cell-free DNA analysis and immune repertoire sequencing from the same test sample according to one embodiment of the present disclosure.

[0102] Figure 6 Graphs of TMB scores for samples with lower tumor fraction and lower coverage with (o) and without (Δ) the TMB correction or adjustment methods disclosed herein.

[0103] Figure 7 is a flow chart schematically depicting an exemplary TMB workflow according to some embodiments of the present invention.

[0104] Fig. 8A and Figure 8B is a graph showing the types of mutations associated with non-synonymous coding single nucleotide variants (SNVs). Specifically, Fig. 8Ais a graph showing synonymous SNVs correlated with non-synonymous SNVs (Pearson's r = 0.90; number of non-synonymous SNVs (x-axis); number of synonymous SNVs (y-axis)). Figure 8B Figure 2 shows indels associated with nonsynonymous SNVs (Pearson's r = 0.71; number of nonsynonymous SNVs (x-axis); number of indels (y-axis)). Figure 8C It is a graph showing the correlation between the ratio of intronic SNVs and the ratio of exonic SNVs (Pearson's r=0.89; ratio of intronic SNVs (x-axis); ratio of exonic SNVs (y-axis)).

[0105] Figure 9A-9D is a violin plot showing that correction for tumor dropout in large panel assays eliminates the effect of mutation counts on ( Fig.9A (Maximum MAF bin (%) (x-axis); mutation count (y-axis)) Tumor shedding and ( Fig. 9B (molecular coverage (x1000)) (x-axis); mutation count (y-axis))) dependence of input cfDNA, resulting in pTMB being largely independent of these input metrics ( Fig. 9C (Maximum MAF bin (%) (x-axis); TMB (mut / Mb) (y-axis)) and Fig.9D (Molecular coverage (x1000) (x-axis); TMB (mut / Mb) (y-axis)).

[0106] Fig. 10A (TMB (mut / Mb) (x-axis); sample fraction (%) (y-axis)) and Fig. 10B (TMB(mut / Mb) (x-axis); Tumor type (y-axis)) are graphs showing the distribution of TMB within a cohort and between tumor types, respectively.

[0107] Fig.11A (Principal component (PC) 1 (x-axis); PC2 (y-axis)) and Fig. 11B (TMB (mut / Mb) (x-axis); Race (y-axis)) are graphs showing principal component analysis (PCA) clustering and TMB scores, respectively.

[0108] Fig.12 is a graph showing the correlation of TMB with oncogenic mutations (TMB(mut / Mb) (x-axis); driver mutations (y-axis)).

[0109] Fig.13A Figure 2 is a graph showing that the clonality of somatic mutations and chromosomal instability are highly variable across the TMB landscape. Fig. 13Bis a graph showing detection of high-scoring microsatellite instability (MSI-high) in a subset of TMB-high samples.

[0110] definition

[0111] In order to more easily understand the present disclosure, some terms are first defined below. The following terms and other definitions of other terms can be set forth through the specification. If the definition of the term set forth below is inconsistent with the definition in the application or patent incorporated by reference, the definition set forth in the present application should be used to understand the meaning of the term.

[0112] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a method" includes one or more methods and / or steps of the type described herein and / or which will become apparent to one of ordinary skill in the art upon reading this disclosure and so forth.

[0113] It should also be understood that the terms used herein are only for the purpose of describing specific embodiments and are not intended to be limiting. In addition, unless otherwise defined, all technical terms and scientific terms used herein have the same meanings as those commonly understood by those of ordinary skill in the art to which the present disclosure belongs. When describing and claiming these methods, computer-readable media, and systems, the following terms and their grammatical variants will be used according to the definitions set forth below.

[0114] About. As used herein, "about" or "approximately" applied to one or more values ​​or elements of interest refers to a value or element similar to the reference value or element. In certain embodiments, the term "about" or "approximately" refers to a value or element that falls within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1% or less in either direction of the reference value or element, unless otherwise specified or obvious from the context (unless the number will exceed 100% of the possible value or element).

[0115] Adjusted tumor mutational burden: As used herein, "adjusted tumor mutational burden," "adjusted tumor mutation burden," or "adjusted outcome" refers to observed mutation counts that have been corrected to account for tumor fraction, coverage, and / or another sequencing parameter.

[0116] Administration: As used herein, "administering" or "administering" a therapeutic agent (e.g., an immunotherapeutic agent) to a subject refers to giving, providing, or contacting a composition with a subject. Administration can be accomplished by any of a number of routes, including, for example, topical, oral, subcutaneous, intramuscular, intraperitoneal, intravenous, intrathecal, and intradermal.

[0117] Adaptor. As used herein, "adaptor" refers to a short nucleic acid (e.g., less than about 500 nucleotides, less than about 100 nucleotides, or less than about 50 nucleotides in length) that is usually at least partially double-stranded and used to connect either end or both ends of a given sample nucleic acid molecule. An adaptor may include a primer binding site that allows amplification of nucleic acid molecules that are both adaptors on both ends, and / or a sequencing primer binding site, including primer binding sites for sequencing applications such as various next generation sequencing (NGS) applications. An adaptor may also include a binding site for a capture probe, such as an oligonucleotide attached to a flow cell support, etc. An adaptor may also include a nucleic acid tag as described herein. Nucleic acid tags are typically positioned relative to amplification primers and sequencing primer binding sites so that the nucleic acid tag is included in the amplicon and sequencing reads of a given nucleic acid molecule. The same or different adaptors may be connected to the corresponding ends of a nucleic acid molecule. In some embodiments, in addition to different nucleic acid tags, the same adaptor is connected to the corresponding ends of a nucleic acid molecule. In some embodiments, the adapter is a Y-shaped adapter, one end of which is flat-ended or tailed as described herein for connection to a nucleic acid molecule, which is also flat-ended or tailed with one or more complementary nucleotides. In yet other exemplary embodiments, the adapter is a bell-shaped adapter, comprising a flat-ended or tailed end for connection to the nucleic acid molecule to be analyzed. Other examples of adapters include T-tailed and C-tailed adapters.

[0118] Amplification. As used herein, "amplify" or "amplification" in the context of nucleic acids refers to the production of multiple copies of a polynucleotide or a portion of a polynucleotide, typically starting from a small amount of polynucleotide (e.g., a single polynucleotide molecule), wherein the amplification product or amplicon is typically detectable. Amplification of polynucleotides includes various chemical and enzymatic processes.

[0119] Barcode. As used herein, a "barcode" or "molecular barcode" in the context of nucleic acids refers to a nucleic acid molecule comprising a sequence that can be used as a molecular identifier. For example, during next-generation sequencing (NGS) library preparation, a single "barcode" sequence is typically added to each DNA fragment so that each read can be identified and sorted prior to final data analysis.

[0120] Cancer type. As used herein, "cancer type" refers to a type or subtype of cancer, for example, as defined by histopathology. Cancer type can be defined by any conventional criteria, such as based on occurrence in a given tissue (e.g., blood cancer, central nervous system (CNS) cancer, brain cancer, lung cancer (small cell and non-small cell), skin cancer, nose cancer, laryngeal cancer, liver cancer, bone cancer, lymphoma, pancreatic cancer, intestinal cancer, rectal cancer, thyroid cancer, bladder cancer, kidney cancer, oral cancer, stomach cancer, breast cancer, prostate cancer, ovarian cancer, lung cancer, intestinal cancer, soft tissue cancer, neuroendocrine cancer, gastroesophageal cancer, head and neck cancer, gynecological cancer, colorectal cancer, Cancer can also be classified by stage (e.g., 1, 2, 3 or 4 phases) and whether it is primary or secondary origin.

[0121] Cell-free nucleic acid. As used herein, "cell-free nucleic acid" refers to nucleic acids that are not contained in cells or otherwise bound to cells, or in some embodiments, refers to nucleic acids that are retained in a sample after the removal of intact cells. Cell-free nucleic acids may include, for example, all unencapsulated nucleic acids derived from body fluids from a subject (e.g., blood, plasma, serum, urine, cerebrospinal fluid (CSF), etc.). Cell-free nucleic acids include DNA (cfDNA), RNA (cfRNA) and hybrids thereof, including genomic DNA, mitochondrial DNA, circulating DNA, siRNA, miRNA, circulating RNA (cRNA), tRNA, rRNA, small nucleolar RNA (snoRNA), Piwi-interacting RNA (piRNA), long non-coding RNA (long ncRNA) and / or fragments of any of these. Cell-free nucleic acids may be double-stranded, single-stranded, or hybrids thereof. Cell-free nucleic acids may be released into body fluids by secretion or cell death programs, such as cell necrosis, apoptosis, etc. Some cell-free nucleic acids are released into body fluids from cancer cells, such as circulating tumor DNA (ctDNA). Others are released from healthy cells. CtDNA can be unencapsulated tumor-derived fragmented DNA. The cell-free nucleic acid can have one or more epigenetic modifications, for example, the cell-free nucleic acid can be acetylated, 5-methylated, ubiquitinated, phosphorylated, sumoylated, ribosylated, and / or citrullinated.

[0122] Cellular nucleic acid. As used herein, "cellular nucleic acid" refers to nucleic acids that are located within one or more cells at least when a sample is taken or collected from a subject, even if such nucleic acids are subsequently removed (e.g., by cell lysis) as part of a given analytical procedure.

[0123] Clonal hematopoietic-derived mutation. As used herein, "clonal hematopoietic-derived mutation" refers to the somatic acquisition of a genomic mutation in a hematopoietic stem and / or progenitor cell that results in clonal expansion.

[0124] Clonotype. As used herein, "clonotype" in the context of an immune cell receptor refers to a unique nucleotide sequence resulting from a mutation or gene rearrangement process of the nucleotide sequence encoding the receptor (e.g., a unique nucleotide sequence encoding the CDR3 sequence of a T cell receptor (TCR) polypeptide chain).

[0125] Comparator results. As used herein, "comparator results" refers to a result or a set of results, and a given test sample or test result can be compared with it to identify one or more possible characteristics of the test sample or result, and / or to identify one or more possible prognostic results and / or one or more customized treatment methods for the subject from which the test sample is obtained or otherwise obtained. Comparator results are usually obtained from a group of reference samples (e.g., from a subject with the same cancer type as the test subject and / or from a subject who is receiving or has received the same treatment as the test subject). In certain embodiments, for example, the adjusted TMB score is compared with the comparator result to identify the substantial match between the adjusted TMB score determined for the test sample and the TMB score determined for a group of reference samples. The TMB score determined for the group of reference samples is usually indexed with one or more customized treatments. Therefore, when a substantial match is identified, the corresponding customized treatment is therefore also identified as a potential treatment approach for the subject from which the test sample is obtained.

[0126] Confidence interval. As used herein, a "confidence interval" refers to a range of values ​​so defined that the value of a given parameter lies within the range of values ​​with a certain probability.

[0127] Control sample: As used herein, "control sample" refers to a sample of known composition and / or with known properties and / or known parameters (e.g., known tumor fraction, known coverage, known TMB, etc.) that is analyzed or compared with a test sample to evaluate the accuracy of the analytical procedure. In some embodiments, the control sample used in the control sample data set can be cancer type specific and / or treatment specific.

[0128] As used herein, a "control sample data set" refers to a sample with a tumor score greater than a predetermined 阈值肿瘤分数 The data set of the control samples.

[0129] Copy number variant. As used herein, "copy number variant," "CNV," or "copy number variation" refers to the phenomenon that certain portions of the genome are repeated, and the number of repeats in the genome varies between individuals in the population under consideration, as well as between two conditions or states of an individual (e.g., a CNV in an individual may vary before and after receiving treatment).

[0130] Coverage. As used herein, "coverage" refers to the number of nucleic acid molecules that represent a specific base position.

[0131] Tailored therapy. As used herein, "tailored therapy" refers to a therapy that is associated with a desired treatment outcome for a subject or population of subjects with a given TMB score or within a defined range of TMB scores.

[0132] Deoxyribonucleic acid or ribonucleic acid. As used herein, "deoxyribonucleic acid" or "DNA" refers to a natural or modified nucleotide having a hydrogen group at the 2'-position of the sugar portion. DNA generally includes a nucleotide chain containing four types of nucleotide bases: adenine (A), thymine (T), cytosine (C) and guanine (G). As used herein, "ribonucleic acid" or "RNA" refers to a natural or modified nucleotide having a hydroxyl group at the 2'-position of the sugar portion. RNA generally includes a nucleotide chain containing four types of nucleotides: A, uracil (U), G and C. As used herein, the term "nucleotide" refers to a natural nucleotide or a modified nucleotide. Certain pairs of nucleotides specifically bind to each other in a complementary manner (called complementary base pairing). In DNA, adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). In RNA, adenine (A) pairs with uracil (U) and cytosine (C) pairs with guanine (G). When a first nucleic acid chain is combined with a second nucleic acid chain consisting of nucleotides complementary to the nucleotides in the first chain, the two chains combine to form a double chain. As used herein, "nucleic acid sequencing data", "nucleic acid sequencing information", "sequence information", "nucleic acid sequence", "nucleotide sequence", "genomic sequence", "genetic sequence" or "fragment sequence" or "nucleic acid sequencing read" means any information or data indicating the order and identity of nucleotide bases (e.g., adenine, guanine, cytosine and thymine or uracil) in a molecule (e.g., a whole genome, a whole transcriptome, an exome, an oligonucleotide, a polynucleotide or a fragment) of a nucleic acid such as DNA or RNA. It should be understood that the present teachings contemplate sequence information obtained using all available various techniques, platforms or technologies including but not limited to: capillary electrophoresis, microarrays, ligation-based systems, polymerase-based systems, hybridization-based systems, direct or indirect nucleotide identification systems, pyrophosphate sequencing, ion-based or pH-based detection systems, and electronic signature-based systems.

[0133] Driver mutation. As used herein, "driver mutation" refers to a mutation that drives cancer progression.

[0134] Expected distribution of expected mutation scores. As used herein, "expected distribution of expected mutation scores" refers to the range of expected mutation scores determined by a statistical distribution model (eg, binomial distribution, etc.).

[0135] Expected mutation count. As used herein, "expected mutational count" or "expected mutation count" or "adjusted mutation count" or "adjusted mutational count" refers to the adjusted observed mutation count.

[0136] Expected mutation fraction (f). As used herein, "expected mutation fraction" refers to the fraction of actual somatic mutations called in a sample, which is derived from the sensitivity of the bioinformatics analysis and the distribution of relative MAFs derived from a database of relative MAFs of all mutations determined by bioinformatics analysis in a control sample dataset.

[0137] Anticipated Results. As used herein, an "anticipated result" refers to a possible, hoped-for, or predicted outcome.

[0138] Immune repertoire. As used herein, "immune repertoire" refers to the sum total of T cell receptors and B cell receptors (ie, immunoglobulins) that make up a subject's adaptive immune system.

[0139] Immunotherapy. As used herein, "immunotherapy" refers to treatment with one or more of the following agents, which act to stimulate the immune system to kill cancer cells or at least inhibit the growth of cancer cells, and preferably reduce the further growth of cancer, reduce the size of cancer and / or eliminate cancer. Some such agents bind to targets presented on cancer cells; some bind to targets presented on immune cells rather than cancer cells; some bind to targets presented on both cancer cells and immune cells. Such agents include, but are not limited to, checkpoint inhibitors and / or antibodies. Checkpoint inhibitors are inhibitors of pathways of the immune system that maintain self-tolerance and regulate the duration and amplitude of physiological immune responses in peripheral tissues to minimize para-tissue damage (see, e.g., Pardoll, Nature Reviews Cancer 12, 252–264 (2012)). Exemplary agents include antibodies against any of the following: PD-1, PD-2, PD-L1, PD-L2, CTLA-40, OX40, B7.1, B7He, LAG3, CD137, KIR, CCR5, CD27 or CD40. Other exemplary agents include proinflammatory cytokines such as IL-1β, IL-6 and TNF-α. Other exemplary agents are T cells activated against tumors, such as T cells activated by expressing chimeric antigens that target tumor antigens recognized by the T cells.

[0140] Indel. As used herein, "indel" refers to a mutation involving the insertion or deletion of nucleotides in the genome of a subject.

[0141] Indexed. As used herein, "indexed" refers to a first element (eg, a TMB score) that is associated with a second element (eg, a given treatment).

[0142] Maximum MAF. As used herein, "maximum MAF" or "max MAF" refers to the maximum MAF of all somatic variants in a sample.

[0143] Minor allele frequency. As used herein, "minor allele frequency" refers to the frequency with which a minor allele (e.g., an allele that is not the most common) occurs in a given nucleic acid population, such as a sample obtained from a subject. Genetic variations at low minor allele frequencies typically have a relatively low frequency present in a sample.

[0144] Mutant allele fraction. As used herein, "mutant allele fraction," "mutation dose," or "MAF" refers to the fraction of nucleic acid molecules containing allelic changes or mutations at a given genomic position in a given sample. MAF is typically expressed as a fraction or percentage. For example, MAF is typically less than about 0.5, 0.1, 0.05, or 0.01% (i.e., less than about 50%, 10%, 5%, or 1%) of all somatic variants or alleles present at a given site.

[0145] Mutation. As used herein, "mutation" refers to a variation from a known reference sequence, and includes mutations such as single nucleotide variants (SNVs), copy number variants or variations (CNVs) / aberrations, insertions or deletions (indels), gene fusions, transversions, translocations, frameshifts, duplications, repeat amplifications, and epigenetic variants. Mutations can be germline mutations or somatic mutations. In some embodiments, the reference sequence for comparison purposes is the wild-type genomic sequence of the species of the subject providing the test sample, typically the human genome.

[0146] Mutation caller. As used herein, "mutation caller" refers to an algorithm (typically implemented in software or other computer-implemented manner) for identifying mutations in test sample data (eg, sequence information obtained from a subject).

[0147] Mutation count. As used herein, "mutation count" or "mutational count" refers to the number of somatic mutations in the entire genome or exome or targeted region of a nucleic acid sample.

[0148] Neoantigen-Orphan Immune Receptor Information. As used herein, "neoantigen-orphan immune receptor information" refers to information associated with an antigen that was not previously recognized by the immune system of a given subject. Typically, neoantigen-orphan immune receptor information is obtained from altered polypeptides or encoding polynucleotides formed by one or more tumor-associated mutations. In certain embodiments, neoantigen-orphan immune receptor information is derived from sequence information associated with those altered polypeptides or encoding polynucleotides.

[0149] Neoplasm. As used herein, the terms "neoplasm" and "tumor" are used interchangeably. They refer to an abnormal growth of cells in a subject's body. A neoplasm or tumor can be benign, potentially malignant, or malignant. Malignant tumors are called cancer or cancerous tumors.

[0150] Next Generation Sequencing. As used herein, "Next Generation Sequencing" or "NGS" refers to sequencing technologies with increased throughput compared to traditional Sanger and capillary electrophoresis-based methods, e.g., the ability to generate tens of thousands of relatively small sequence reads at a time. Some examples of next generation sequencing technologies include, but are not limited to, sequencing by synthesis, sequencing by ligation, and sequencing by hybridization.

[0151] Non-synonymous mutation. As used herein, a "non-synonymous mutation" refers to a mutation that changes the amino acid sequence of an encoded polypeptide.

[0152] Nucleic acid tag. As used herein, "nucleic acid tag" refers to a short nucleic acid (e.g., less than about 500 nucleotides, about 100 nucleotides, about 50 nucleotides, or about 10 nucleotides in length) used to distinguish nucleic acids from different samples (e.g., representing a sample index), or different nucleic acid molecules in the same sample (e.g., representing a molecular barcode), short nucleic acids of different types or after different treatments. Nucleic acid tags contain predetermined, fixed, non-random, random or semi-random oligonucleotide sequences. Such nucleic acid tags can be used to label different nucleic acid molecules or different nucleic acid samples or subsamples. Nucleic acid tags can be single-stranded, double-stranded or at least partially double-stranded. Nucleic acid tags optionally have the same length or different lengths. Nucleic acid tags can also include double-stranded molecules with one or more blunt ends, including 5' or 3' single-stranded regions (e.g., overhangs), and / or include one or more other single-stranded regions at other positions within a given molecule. Nucleic acid tags can be attached to one or both ends of other nucleic acids (e.g., sample nucleic acids to be amplified and / or sequenced). Nucleic acid tags can be decoded to reveal information, such as the sample source, form or processing of a given nucleic acid. For example, nucleic acid tags can also be used to realize the collection and / or parallel processing of more than one sample containing nucleic acids with different molecular barcodes and / or sample indexes, wherein the nucleic acid is subsequently deconvoluted by detecting (e.g., reading) nucleic acid tags. Nucleic acid tags can also be referred to as identifiers (e.g., molecular identifiers, sample identifiers). Additionally or alternatively, nucleic acid tags can be used as molecular identifiers (e.g., for distinguishing different molecules or amplicons of different parental molecules in the same sample or subsample). For example, this includes uniquely labeling different nucleic acid molecules in a given sample, or non-uniquely labeling these molecules. In the case of non-uniquely labeling applications, a limited number of labels (i.e., molecular barcodes) can be used to label each nucleic acid molecule, so that different molecules can be distinguished based on their endogenous sequence information (e.g., they are mapped to the starting and / or ending position of the selected reference genome, the subsequence and / or sequence length at one end or both ends of the sequence) in conjunction with at least one molecular barcode. Typically, a sufficient number of different molecular barcodes are used so that the probability that any two molecules may have the same endogenous sequence information (e.g., start and / or end position, subsequence at one or both ends of the sequence, and / or length) and also have the same molecular barcode is low (e.g., less than about 10%, less than about 5%, less than about 1%, or less than about 0.1%).

[0153] Observed mutation count. As used herein, "observed mutation count" or "observed mutational count" refers to the number of somatic mutations determined by the bioinformatics analysis described herein.

[0154] Passenger mutation. As used herein, a "passenger mutation" refers to a mutation that does not alter fitness but occurs in a cell that either coincidentally or subsequently acquires a driver mutation.

[0155] Polynucleotides. As used herein, "polynucleotides", "nucleic acids", "nucleic acid molecules" or "oligonucleotides" refer to linear polymers of nucleosides (including deoxyribonucleosides, ribonucleosides or their analogs) linked by internucleoside bonds. Typically, a polynucleotide contains at least three nucleosides. Oligonucleotides typically range in size from a few monomeric units, e.g., 3-4, to several hundred monomeric units. Unless otherwise noted, whenever a polynucleotide is represented by a letter sequence such as "ATGCCTG", it is understood that the nucleotides are in the order 5'→3' from left to right, and in the case of DNA, "A" represents deoxyadenosine, "C" represents deoxycytidine, "G" represents deoxyguanosine, and "T" represents deoxythymidine. The letters A, C, G and T can be used to refer to the base itself, to the nucleoside or nucleotide containing the base, as is standard in the art.

[0156] Processing. As used herein, the terms "processing," "calculating," and "comparing" are used interchangeably. In certain applications, these terms refer to determining differences, e.g., differences in quantity or sequence. For example, gene expression, copy number variation (CNV), indels, and / or single nucleotide variant (SNV) values ​​or sequences can be processed.

[0157] Relative MAF. As used herein, "relative MAF" refers to an estimate of the MAF for a particular variable compared to the maximum MAF in the sample.

[0158] Reference sequence. As used herein, "reference sequence" refers to a known sequence for the purpose of comparing with an experimentally determined sequence. For example, a known sequence can be a whole genome, a chromosome, or any segment thereof. Reference generally includes at least about 20, at least about 50, at least about 100, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, at least about 500, at least about 1000 or more nucleotides. A reference sequence can be aligned with a single continuous sequence of a genome or chromosome, or can include non-continuous segments aligned with different regions of a genome or chromosome. Exemplary reference sequences include, for example, human genomes, such as hG19 and hG38.

[0159] Sample. As used herein, "sample" refers to anything that can be analyzed by the methods and / or systems disclosed herein.

[0160] Limit of Detection (LoD). As used herein, "limit of detection" refers to the minimum amount of a substance (eg, nucleic acid) in a sample that can be measured by a given assay or analytical method.

[0161] Sensitivity. As used herein, "sensitivity" refers to the probability of detecting the presence of a mutation at a given MAF and coverage.

[0162] Sequencing. As used herein, "sequencing" refers to any of a number of techniques used to determine the sequence (eg, the identity and order of monomeric units) of a biomolecule, eg, a nucleic acid, such as DNA or RNA. Exemplary sequencing methods include, but are not limited to, targeted sequencing, single molecule real-time sequencing, exon or exome sequencing, intron sequencing, electron microscopy-based sequencing, panel sequencing, transistor-mediated sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxy terminator sequencing, whole genome sequencing, hybridization sequencing, pyrosequencing, capillary electrophoresis, duplex sequencing, cycle sequencing, single base extension sequencing, solid phase sequencing, high throughput sequencing, massively parallel signature sequencing, emulsion PCR, low denaturation temperature co-amplification PCR (COLD-PCR), multiplex PCR, reversible dye terminator sequencing, paired end sequencing, near-term sequencing, exonuclease sequencing, ligation sequencing, short read sequencing, single molecule sequencing, synthesis sequencing, real-time sequencing, reverse terminator sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiD TM Sequencing, MS-PET sequencing, and combinations thereof. In some embodiments, sequencing can be performed by a genetic analyzer, such as, for example, a genetic analyzer commercially available from Illumina, Inc., Pacific Biosciences, Inc., or Applied Biosystems / Thermo Fisher Scientific, and the like.

[0163] Sequence information. As used herein, "sequence information" in the context of a nucleic acid polymer refers to the order and identity of the monomer units (eg, nucleotides, etc.) in the polymer.

[0164] Single nucleotide variant. As used herein, "single nucleotide variant" or "SNV" refers to a mutation or variation of a single nucleotide occurring at a specific position in the genome.

[0165] Somatic mutations. As used herein, "somatic mutations" refer to mutations in the genome that occur after conception. Somatic mutations can occur in any cell of the body except germ cells and are therefore not passed on to future generations.

[0166] Substantial match. As used herein, "substantial match" means that at least a first value or element is at least approximately equal to at least a second value or element. In certain embodiments, for example, when there is at least a substantial or approximate match between an adjusted result (e.g., an adjusted TMB score) and a comparator result (e.g., a TMB score determined from one or more control or reference samples), a customized treatment is identified.

[0167] Subject. As used herein, "subject" refers to an animal, such as a mammalian species (e.g., human) or avian (e.g., bird) species or other organisms, such as plants. More specifically, the subject can be a vertebrate, for example a mammal such as a mouse, a primate, an ape or a human. Animals include farm animals (e.g., production cattle, dairy cows, poultry, horses, pigs, etc.), sports animals, and companion animals (e.g., pets or support animals). The subject can be a healthy individual, an individual with or suspected of having a disease or a tendency to suffer from the disease, or an individual in need of treatment or suspected of needing treatment. The term "individual" or "patient" is intended to be used interchangeably with "subject".

[0168] For example, the subject can be an individual who has been diagnosed with cancer, will receive cancer treatment and / or has received at least one cancer treatment. The subject can be in remission of cancer. As another example, the subject can be an individual diagnosed with an autoimmune disease. As another example, the subject can be a female individual who is pregnant or planning to become pregnant, who may have been diagnosed or suspected of having a disease, such as cancer, an autoimmune disease.

[0169] Synonymous mutation. As used herein, a "synonymous mutation" refers to a mutation that does not change the amino acid sequence of the encoded polypeptide.

[0170] Threshold. As used herein, "threshold" refers to a predetermined value of experimentally determined values ​​of the same parameter used to characterize different samples, depending on their relationship to the threshold value.

[0171] Threshold Maximum MAF :As used herein, “threshold maximum MAF ” refers to a predetermined value of the maximum MAF for characterizing the experimentally determined maximum MAF of different samples. In some embodiments, for example, the threshold maximum MAF It may be about 0.5%, 1%, 2%, 5%, 10% or another selected threshold.

[0172] Threshold TMB 评分 As used herein, “threshold TMB 评分 " refers to a predetermined value of the TMB score that is used to characterize the experimentally determined value of the TMB score for different samples. In certain embodiments, for example, a threshold TMB评分 It may be about 5, 10, 15, 20, 25, 30, 35, 40 or another selected threshold.

[0173] Threshold tumor 分数 As used herein, "threshold tumor 分数 " refers to a predetermined value of tumor fraction that is used to characterize experimentally determined values ​​of tumor fraction for different samples. In certain embodiments, for example, a threshold tumor fraction 分数 It may be about 1%, 2%, 3%, 4%, 5% or another selected threshold.

[0174] Threshold f : As used herein, “threshold f " refers to a predetermined value of the expected mutation score (f) used to characterize experimentally determined values ​​of the expected mutation score for different samples. In some embodiments, for example, the threshold f It may be greater than about 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or another selected threshold.

[0175] Tumor score. As used herein, "tumor score" refers to an estimate of the fraction of nucleic acid molecules derived from a tumor in a given sample. For example, the tumor score of a sample can be a measure derived from the maximum MAF of the sample or the sequencing coverage pattern of the sample or the length of cfDNA fragments in the sample or any other selected feature of the sample. In some cases, the tumor score of a sample is equal to the maximum MAF of the sample.

[0176] Tumor mutation load. As used herein, the terms "tumor mutation burden (TMB)", "tumor mutational burden (TMB)" or "cancer mutation burden (cancermutation burden)" or "mutational load" or "mutation load (mutation load)" are used interchangeably. They refer to the total number of mutations, such as somatic mutations, present in the sequenced portion of the tumor genome. TMB can refer to the number of coding, base substitutions, insertions, deletions, or other mutations in the targeted region of the tumor genome or exome or genome examined per megabase. They can indicate the detection, evaluation, calculation, or prediction of sensitivity and / or tolerance to cancer therapeutic agents or drugs such as immune checkpoint inhibitors, antibodies, etc. Tumors with higher levels of TMB may express more neoantigens (a type of cancer-specific antigen), which may allow a more robust immune response and therefore allow a more durable response to immunotherapy. Because the immune system relies on a sufficient number of neoantigens to respond appropriately, the number of somatic mutations can be used as a proxy for determining the number of neoantigens in a tumor. TMB can be used to infer the robustness of the immune response to a drug treatment in a subject and the efficacy of the drug treatment. Germline variants and somatic variants can be distinguished bioinformatically to identify antigenic somatic variants.

[0177] Variant: As used herein, "variant" may be referred to as an allele. Variants typically occur at a frequency of 50% (0.5) or 100% (1), depending on whether the allele is heterozygous or homozygous. For example, germline variants are inherited and typically have a frequency of 0.5 or 1. However, somatic variants are acquired variants and typically have a frequency of <0.5. The major allele and minor allele of a genetic locus refer to nucleic acids having a locus in which the locus is occupied by nucleotides of a reference sequence and variant nucleotides that are different from the reference sequence, respectively. Measurements at a locus may take the form of an allele fraction (AF), which measures the frequency at which an allele is observed in a sample.

[0178] Detailed Description

[0179] introduction

[0180] Cancer comprises a large group of genetic diseases that share the common characteristic of abnormal cell growth with the potential to metastasize beyond the cell's original site in the body. The underlying molecular basis of the disease is mutational and / or epigenetic changes that result in a transformed cellular phenotype, whether these deleterious changes are inherited or have a somatic basis. Further complicating matters, these molecular changes are often heterogeneous, not only between patients with the same type of cancer, but even within a given patient's own tumor.

[0181] Given the mutational variability observed in most cancers, one of the challenges of cancer care is to identify the treatment to which patients are most likely to respond, given their individual cancer type. Various biomarkers are used to match cancer patients to appropriate treatments, including cancer immunotherapy. One biomarker of response is tumor mutation burden (TMB), which is a quantitative measure of the total number of mutations per coding region of a given cancer genome. To date, the application of such response biomarkers has been limited in part by the methods used to measure and analyze TMB.

[0182] The present disclosure provides methods, computer readable media and systems that can be used to determine and analyze the TMB in patient samples and help guide cancer treatment decisions. Traditionally, when tumor score (for example, mutant allele score (MAF)) and / or coverage rate are low, the TMB obtained by calculating mutation rate is usually inaccurate because the assay sensitivity for calling mutations is reduced. Therefore, in some aspects, the TMB observed is adjusted according to various measures of assay sensitivity, such as tumor score (it sets the MAF of mutations in a given sample), coverage rate, etc. For example, in the absence of such adjustment, it is actually TMB-high, but samples with low tumor score and / or low coverage rate are often erroneously reported as TMB-low. When making treatment decisions according to such results, such results may have a significant downstream impact on patients.

[0183] Tumor mutation burden adjustment method

[0184] The application discloses various methods for adjusting TMB, to solve the variability of tumor score and / or coverage in a given assay, otherwise the variability may cause inaccurate TMB reports. In certain embodiments, the method includes adjusting the original somatic mutation count (for example, SNV and insertion and deletion called by a given bioinformatics pipeline or workflow) by the prediction of the score of the actual mutation of the sample that the specific bioinformatics pipeline on the group under consideration is called. In certain embodiments, the model uses the logic and binomial sampling solution of bioinformatics mutation (for example, SNV, insertion and deletion, etc.) caller to calculate the sensitivity of the mutation caller on the expected distribution of MAF in the sample coverage setting and / or the sample. In some embodiments, the expected MAF distribution in the sample is derived from the relative MAF of all mutations called in the control sample of the control sample data set. In some of these embodiments, the model calculates the expected score of the observed mutation, and the probability distribution of the score, which can be summarized as 95% confidence intervals of such score. This can be used to output a high sensitivity (e.g., the highest possible actual mutation count) and / or high specificity (e.g., the lowest possible actual mutation count) calculation of the expected mutation count. In some of these embodiments, the expected mutation count is then divided by the size of the genomic region analyzed to give a mutation rate (i.e., TMB or TMB score). Compared to the "gold standard" of TMB or the TMB calculated if the sample is in a high tumor score, the TMB calculated by implementing the adjustments described herein will be more accurate than the TMB measured without these adjustments, and the "gold standard" is calculated from, for example, complete exome sequencing of a tissue sample from a tumor.

[0185] To further illustrate, Figure 1 A flow chart schematically depicting exemplary method steps for adjusting TMB according to some embodiments of the present invention is provided. As shown, method 100 includes determining an observed mutation count from sequence information obtained from one or more nucleic acids in a sample from a subject in step 110. Method 100 also includes determining the coverage of the tumor score and / or nucleic acid to generate sequencing parameters in step 112, and determining the expected mutation score and / or the expected distribution of the expected mutation score for a given sequencing parameter in step 114 to generate an expected result. In addition, method 100 also includes adjusting the observed mutation count in the case of a given expected result in step 116 to generate an adjusted result, thereby determining the TMB of the subject.

[0186] In certain embodiments, method 100 includes additional upstream and / or downstream steps. In some embodiments, for example, method 100 begins at step 102, and in step 104, a sample from a subject is provided (e.g., a blood sample taken from a subject is provided). In these embodiments, the workflow of method 100 generally further includes amplifying nucleic acids in the sample to generate amplified nucleic acids in step 106, and sequencing the amplified nucleic acids in step 108 to generate sequence information, and then determining the observed mutation counts based on the sequence information in step 110. Nucleic acid amplification (including related sample preparation), nucleic acid sequencing, and related data analysis are also described herein.

[0187] In some embodiments, method 100 includes various steps downstream of the adjusted results generated in step 116. Some examples of these include comparing the adjusted results with one or more comparator results indexed with one or more treatments in step 118 to identify one or more customized treatments for the subject. In some embodiments, method 100 includes reporting the results to the subject or a physician. In other exemplary embodiments, method 100 also includes administering at least one identified customized treatment to the subject (e.g., to treat cancer or another disease or condition in the subject) when there is a substantial match between the adjusted results and the comparator results in step 120 before ending in step 122.

[0188] To further illustrate, for any given sample, the tumor score (e.g., the maximum MAF of somatic mutations ("minor AF") or another estimate of the tumor score) and / or some indicators of coverage (e.g., the unique molecular median of each base) are input into the model. The model uses calculations based on the mutation (e.g., SNV, insertion and deletion, etc.) calling algorithm used to output the score of mutations in the group space expected to be called under these conditions, and the distribution of the score of mutations expected to be observed (e.g., 95% confidence interval of the range of the score of mutations expected to be observed can be summarized). In some embodiments, the expected score of the actual mutation called by bioinformatics analysis on a given group is calculated by evaluating the probability of calling a mutation with a given MAF (i.e., sensitivity) within the expected MAF range (i.e., converting MAF into a distribution of relative MAF by multiplying MAF by the sample tumor score).

[0189] Typically, the sensitivity of the mutation caller uses the algorithm of bioinformatics analysis described herein, and is estimated from the probability of calling mutations at a given MAF and coverage. In some embodiments, the probability can be calculated using an empirical distribution based on previous data (i.e., such a running experiment: wherein the sample has a known mutation at different MAFs of different coverages, and the frequency of detecting mutations is tested, which provides an "empirical" mutation call sensitivity). In some embodiments, the probability can be calculated using a binomial distribution. In some embodiments, more than one requirement based on mutation calls (e.g., some other considerations of the number of molecules supporting mutations and previous expectations of mutations; using mutation call sensitivity and whether mutations are present in cancer hot spots-a combination based on all these components can be used) multi-component distribution to calculate probability. In some embodiments, the sensitivity of the mutation caller can be estimated based on specific basic requirements, such as the type of variant (e.g., SNV, short indels or long indels), genomic background (e.g., hot spot regions, main chain regions, local GC content, etc.) or sample context (e.g., sequencing metrics, such as GC, MAPD, coverage profiles; or sample metrics of tumor types).

[0190] In some embodiments, the method includes defining the expected distribution of MAF in a particular sample. In some of these embodiments, the distribution of relative MAF is empirically fitted to the curve of all control samples in the control sample data set. The empirical fit curve is described by the following equation:

[0191] F = 1 / (1+(P_50 / relative-MAF) n ),

[0192] Wherein F is cumulative distribution function, P_50 is relative MAF median, relative-MAF is relative MAF, and n is the index of fitting relative distribution shape.In certain embodiments, the expected distribution of expected mutation score is obtained from one or more data sets of relative mutant allele fraction (MAF) observed in at least one control sample data set.The control sample data set generally includes at least about 25 to at least about 30,000 or more control samples.In some embodiments, the control sample data set includes about 50,75,100,150,200,300,400,500,600,700,800,900,1,000,2,500,5,000,7,500,10,000,15,000,20,000,25,000,50,000,100,000,1,000,000 or more control samples. In some embodiments, the maximum MAF observed in the control sample comprises about 0.5%, about 1%, about 2%, about 5%, about 10%, or more. In some embodiments, the threshold value is used for the maximum MAF (i.e., the threshold maximumMAF ). In these embodiments, the threshold tumor 分数 Typically it is about 1%, 2%, 3%, 4%, 5% or another selected threshold.

[0193] In some embodiments, the following equation is used to determine the expected result:

[0194] Observed mutation fraction (f) = ∑ MAF (P(called mutation|MAF)*P(mutation at MAF)), where P is probability, MAF is mutant allele fraction, P(called mutation|MAF) is the probability of calling a mutation at a specific MAF, and it represents the sensitivity of the mutation caller and the coverage of the sample at a specific MAF, and P(mutation at MAF) is the probability of mutation at MAF, and it represents the expected distribution of MAF for the sample.

[0195] Since this score is essentially a binomial sampling probability, the upper and lower bounds of the distribution (and confidence interval) of this score are estimated using a binomial proportion confidence interval:

[0196] and

[0197]

[0198] Where f is the expected fraction of mutations called, n_true is the expected actual mutation count, equal to the number of mutations observed given f, and z is the confidence level (e.g., 1.96 for a 95% confidence interval). The calculation of the expected actual mutation count is provided by the following equation: n_true = n_observe / f. Thus, for example, if 6 mutations are observed (i.e., n_observe = 6), and 60% of the mutations are observed (i.e., f = 60%), then the expected actual mutation count is 10 (i.e., n_true = 10). In certain embodiments, if f Less than threshold f , then TMB is not determined, whereas in other embodiments, if f Greater than threshold f , then TMB is determined. In some embodiments, for example, the threshold f The expected mutation score (f) and / or the expected distribution of expected mutation scores include a confidence interval of about 95% or more of the mutation score. In some embodiments, the method includes using an upper limit (f) of the 95% confidence interval of the expected mutation score. 上限 ) to generate a lower bound for the adjusted mutation count.

[0199] In some of these embodiments, the raw somatic mutation count (observed mutation count) is then divided by this output of the model to obtain the expected number of mutations that will be called on the panel if the sample is at a high tumor fraction and / or high coverage. The estimate of the fraction can be used as a best estimate of the actual / expected mutation count. Optionally, the upper limit of the 95% confidence interval of the fraction (f 上限 ) can be used to obtain a lower bound on the actual mutation count (i.e., there is at least a 95% chance that the actual mutation count is equal to this mutation count), which is an output of higher specificity (high specificity of reporting TMB-high). In some of these embodiments, the expected / adjusted mutation count is then divided by the size of the genomic region analyzed to give a TMB score for the sample. In some embodiments, if the TMB score of the sample is greater than the threshold TMB 评分 , then the sample is classified as TMB-high. If the TMB score of the sample is below the threshold TMB 评分 , then the sample is classified as a TMB-low sample. In certain embodiments, for example, the threshold TMB 评分 It may be about 5, 10, 15, 20, 25, 30, 35, 40 or another selected threshold.

[0200] Typically, the mutation counts and / or tumor scores observed include many somatic mutations identified in nucleic acids. In some of these embodiments, the mutation counts and / or tumor scores observed exclude one or more known cancer drivers and / or passenger mutations. In certain embodiments, the mutation counts and / or tumor scores observed include many synonymous mutations, many non-synonymous mutations and / or many non-coding mutations identified in nucleic acids. Optionally, the mutation counts and / or tumor scores observed include many mutations, including single nucleotide variants (SNVs), insertions or deletions (indels), copy number variants (CNVs), fusions, transversions, translocations, frameshifts, repetitions, repeat amplifications, epigenetic variants, etc. In certain embodiments, the mutation counts and / or tumor scores observed do not include mutations derived from clonal hematopoiesis. Optionally, the expected mutation score is used as the observed score of the actual mutation count.

[0201] In certain embodiments, an alternative method of incorporating tumor scores is used, for example, in addition to using the maximum mutant allele fraction (maximum MAF) of all somatic mutations (e.g., derived from SNV and insertion and deletion data). Some exemplary alternatives include using a tumor score based on coverage, a tumor score based on cfDNA fragment length, a tumor score based on germline and / or somatic variants, or any combination of these methods. In some embodiments, the sequencing coverage pattern of a predetermined set of genomic regions and the standard maximum likelihood method can be used to estimate the tumor score. In some embodiments, the tumor score can be estimated from the difference in the cfDNA fragment size distribution of the region with copy number changes in the predetermined set of genomic regions and the sample. In some embodiments, the tumor score can be estimated by adjusting the MAF of germline and / or somatic variants to the copy number changes observed in the sample. In some embodiments, the sequencing coverage of a probe for capturing a group of cfDNA molecules in the target genomic region can be used to estimate the tumor score (i.e., somatic score). In some embodiments, the tumor score can be estimated based on the methylation state of the cfDNA molecules in the sample. In some embodiments, the tumor score can be estimated using the maximum MAF, but adjusted for the copy number of a specific genomic position. In some embodiments, somatic MAF in a sample can be combined for tumor fraction estimation. In some embodiments, methylation can be used to estimate tumor fraction based on identifying tumor-derived molecules using methylation patterns, and thus used to identify the fraction of tumor molecules to estimate tumor fraction. In some cases, a combination of all of the above embodiments or a combination of at least a subset of the above embodiments can be used in a model to estimate tumor fraction.

[0202] In some embodiments, the tumor fraction of a given sample may be less than about 0.05%, about 0.1%, about 0.2%, about 0.5%, 1%, about 2%, about 3%, about 4%, about 5% of all nucleic acids.

[0203] In some embodiments, germline variants are excluded from determining TMB scores. In some embodiments, the germline / somatic state of a variant can be determined using a β-binomial distribution model. The β-binomial distribution is used to model the mean and variance of the mutant allele counts of the common germline single nucleotide polymorphism (SNP) located near the candidate variant. If the candidate variant deviates from the distribution of these local germline SNPs, the variant will be called a "somatic variant", otherwise, the variant will be called a "germline variant". The methods and systems described in PCT / US2018 / 052087 and U.S. Provisional Applications 62 / 726,182, 62 / 823,578 and 62 / 857,048 are incorporated by reference.

[0204] Coverage is also optionally incorporated into the model by various means. In some embodiments, coverage can be incorporated as the median of the unique cfDNA fragments of each base. In these embodiments, the model can be made base-specific, and the method includes using the number of unique cfDNA fragments at each base to calculate the sensitivity at the base. In certain embodiments, the method includes identifying many unique sequencing reads containing a given nucleotide position in the nucleic acid to determine the coverage. In yet other exemplary embodiments, the method includes identifying the median number of unique cell-free DNA (cfDNA) molecules containing a given nucleotide position in the nucleic acid to determine the coverage. Coverage can be at least 10, 50, 100, 500, 1000, 5000, 10,000, 20,000 or 50,000 cfDNA fragments at the base position.

[0205] The methods disclosed in the present application generally include obtaining sequence information from a nucleic acid in a sample taken from a subject. In certain embodiments, the sequence information is obtained from a targeted segment of the nucleic acid. Essentially any number of genomic regions are optionally targeted. The targeted segment may include at least 10, at least 50, at least 100, at least 500, at least 1000, at least 2000, at least 5000, at least 10,000, at least 20,000, at least 50,000, or at least 100,000 (e.g., 25, 50, 75, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900, 4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000, , 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000, 15,000, 25,000, 30,000, 35,000, 40,000, 45,000, 50,000, or 100,000) different and / or overlapping genomic regions.

[0206] In certain embodiments, the method includes dividing the observed mutation count by the expected result to produce an adjusted result. Typically, the adjusted result includes many mutations detected in a series of mutant allele fractions in the nucleic acid. The adjusted result typically includes a prediction of the highest possible actual mutation count and / or a prediction of the lowest possible actual mutation count. In certain embodiments, the expected result includes an expected mutation score. In certain embodiments, the adjusted result includes the adjusted mutation count. Typically, the adjusted mutation count is greater than or equal to the observed mutation count.

[0207] In other exemplary embodiments, the counting of somatic mutations (e.g., SNVs, indels, etc.) excludes driver mutations that do not represent background exome mutation rates or TMBs, etc. In some embodiments, the counting of somatic mutations excludes mutations that may be from clonal hematopoiesis, rather than the tumor under consideration. Based on literature and cancer databases (e.g., COSMIC), a selected list of mutations frequently observed in blood-related cancers can be used to identify mutations derived from clonal hematopoiesis. In some embodiments, clonal hematopoietic mutations can be identified using their context (e.g., MAF) in a sample (e.g., other clonal hematopoietic variants are present in a similar MAF or similar MAF range), or by analyzing clonal hematopoietic mutations in a sample database previously studied. In some embodiments, clonal hematopoietic mutations can be identified by sequencing DNA in a patient sample (e.g., white blood cells) derived from blood. In certain embodiments, the counting of somatic mutations also includes SNVs and indels at sites that are not reported as part of the specific group used. For example, this mutation can be in a region (e.g., intron) where a mutation is "unreported" in a given application. This means, for example, that the group used to count somatic mutations is larger, so the sampling error is smaller, and the mutation rate signal is higher. In some embodiments, clonal hematopoietic derived mutations can be identified by using a probability / likelihood model that uses at least some of these parameters as inputs-patient age, tumor type, methylation status at the position, molecular fragment size supporting the mutation, and any other mutations in the sample. In some embodiments, these input parameters can be used to establish a model or prior for whether each mutation is a clonal hematopoietic derived mutation. In some embodiments, machine learning algorithms such as logistic regression, random forests, etc., can be applied to identify clonal hematopoietic derived mutations. The methods and systems described in PCT / US2019 / 035214 can also be used and incorporated by reference.

[0208] In other exemplary embodiments, the method includes using the following mutations of the pooled evidence, the mutation falls below the detection limit (LOD) of any particular SNV, insertion and deletion and / or other types of mutations, but gives an indication of the possibility of mutations higher than most other bases at this base. In these embodiments, TMB-high samples generally have more such evidence than TMB-low samples. Some embodiments include expanding the number of sites with mutations associated with TMB on a given group and / or further improving the estimation of tumor scores. In certain embodiments, by including potential high-information sites of fragmentomics, the estimation or determination of tumor scores is also further improved.

[0209] In some embodiments, the observed mutation count excludes subclonal mutations by filtering out somatic mutations with low MAF. In some embodiments, the observed mutation count does not include somatic mutations that are less than about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 15%, about 20%, about 25%, or about 30% of the maximum MAF.

[0210] In some embodiments, the expected / adjusted mutation counts are adjusted using an exome calibration factor, wherein the exome calibration factor is the ratio of the mutation rate of the analyzed group to the exome mutation rate. In some embodiments, the expected / adjusted mutation counts are divided by the exome calibration factor. In some embodiments, the exome calibration factor is 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09 or at least 1.10. In some embodiments, the exome calibration factor is determined from the exome mutation rate of samples in a cancer database, such as TCGA (The Cancer Genome Atlas). In some embodiments, the exome calibration factor can be specific to a cancer type, and the exome mutation rate can be determined from a sample (in a database) with a specific cancer type.

[0211] In some embodiments, the observed mutation count excludes resistance mutations. In a few embodiments, resistance mutations can be identified using a curated list of mutations frequently observed in patient samples based on the literature and cancer databases (e.g., COSMIC, TCGA). In some embodiments, resistance mutations can be identified by analyzing a database of previously studied samples.

[0212] In some embodiments, resistance mechanisms or any other process can introduce a large number of mutations in a gene (e.g., KRAS in CRC or BRCA1 / 2 reversal in prostate cancer treated with PARP inhibitors), and counting all of these mutations in the observed mutation counts may not reflect the entire exome mutation rate. In such embodiments, the observed mutation counts exclude some mutations in a particular gene if the number of mutations in the particular gene is significantly greater than the expected number of mutations in the gene based on the total sample mutation count and the gene size in the group. The term "significantly greater" can be evaluated based on a statistical sampling model. For example, if a sample has a mutation rate of 10 mutations per Mb (observed by analyzing a group of cancer-related genes), 5 of which are in the KRAS gene (in the group), the mutation rate within KRAS is much higher than expected based on the sample mutation rate. The number of mutations from KRAS counted in the observed mutation count will be suppressed to be closer to the expected rate based on the sample mutation rate.

[0213] In some embodiments, TMB scores can be used to predict whether a patient will respond to immunotherapy. In certain embodiments, the presence of a mutation of a specific type in certain genes (e.g., loss of function of STK11, KEAP1, PTEN, etc.) and / or a patient's mutation signature in a group of genes analyzed (e.g., a patient has a C>T transition) can be used in conjunction with TMB scores to predict whether the patient will respond to immunotherapy treatment. In some embodiments, if a patient has loss of function of certain genes (such as but not limited to STK11, KEAP1, and PTEN), regardless of the TMB score, the patient will not respond to immunotherapy. For example, if a patient has (i) a high TMB score and (ii) STK11 loss of function, loss of function / driver mutations are preferred, and the patient will not respond to treatment.

[0214] In some embodiments, the adjustment methods disclosed herein include obtaining a sample from a subject (e.g., a human or other mammalian subject). Exemplary sample types that are optionally used are also described herein. Essentially any type of nucleic acid (e.g., DNA and / or RNA) can be evaluated according to the methods disclosed in this application. Some examples include cell-free nucleic acids (e.g., cfDNA derived from tumors, etc.), cellular nucleic acids, including circulating tumor cells (e.g., obtained by lysing intact cells in a sample), circulating tumor nucleic acids.

[0215] In some embodiments, the TMB correction model cannot be applied to samples with tumor fractions or any parameters indicating tumor shedding below a specific cutoff value and / or with coverage below a specific cutoff value, which will result in very low mutation expected scores. In some embodiments, the TMB correction model cannot be applied to samples mainly comprising CHIP mutations, which will interfere with accurate TMB estimation. The standard for checking whether the TMB correction model is applicable to a sample includes a method for calculating whether the sample mainly comprises CHIP mutations. Such methods include, for example: (a) a high proportion of mutations are known to be CHIP (using a selected database, or other methods for identifying CHIP, such as sequencing (buffy), fragment omics, etc.); (b) lack of evidence of solid tumors, for example, known driver mutations of sample tumor types; (c) any combination of the above. In some embodiments, the TMB correction model cannot be applied to samples comprising low tumor shedding or mainly CHIP.

[0216] In these embodiments, the method typically also includes various sample or library preparation steps to prepare nucleic acids for sequencing. Many other sample preparation techniques are well known to those skilled in the art. Basically, any of these techniques is used or suitable for use when performing the methods described herein. For example, in addition to various purification steps to separate nucleic acids from other components in a given sample, typical steps for preparing nucleic acids for sequencing include labeling nucleic acids with molecular barcodes, adding adapters (e.g., which may include molecular barcodes), amplifying nucleic acids one or more times, enriching targeted segments of nucleic acids (e.g., using various target capture strategies, etc.), and the like. An exemplary library preparation process is also described herein. Additional details about nucleic acid sample / library preparation are also described, for example, in the following: van Dijk et al., Library preparation methods for next-generation sequencing: Tone down the bias, Experimental Cell Research, 322 (1): 12-20 (2014), Micic (Ed.), Sample Preparation Techniques for Soil, Plant, and Animal Samples (Springer Protocols Handbooks), 1 st Ed., Humana Press (2016), and Chiu, Next-Generation Sequencing and Sequence Data Analysis, Bentham Science Publishers (2018), are each incorporated by reference in their entirety.

[0217] The adjusted TMB determined by the methods disclosed herein is optionally used to diagnose a disease or condition in a subject, particularly the presence of cancer, to characterize such a disease or condition (e.g., staging a given cancer, determining the heterogeneity of the cancer, etc.), monitor the response to treatment, evaluate the potential risk of developing a given disease or condition, and / or assess the prognosis of a disease or condition. The adjusted tumor mutation load is also optionally used to characterize a specific form of cancer. Since cancer is generally heterogeneous in both composition and staging, TMB data can allow for characterization of a specific subtype of cancer, thereby facilitating diagnosis and treatment selection. This information can also provide clues to the prognosis of a specific type of cancer for a subject or healthcare practitioner, and allows a subject or healthcare practitioner to adjust treatment options according to the progression of the disease. Some cancers become more aggressive and genetically unstable as they progress. Other cancers remain benign, inactive, or dormant.

[0218] Adjusted TMB can also be used to determine disease progression and / or monitor recurrence. For example, in some cases, as cancer cell death and the increase in the number of shed nucleic acids, successful treatment may initially increase the adjusted TMB. In these cases, as the treatment progresses, the adjusted TMB will then generally decrease as the tumor size continues to decrease. In other cases, successful treatment can also reduce TMB and / or minor allele scores without an initial increase in tumor mutation load. In addition, if cancer is observed to be relieved after treatment, the adjusted TMB can be used to monitor the recurrence of residual disease or disease in the patient.

[0219] sample

[0220] Sample can be any biological sample separated from a subject.Sample can include body tissue, whole blood, platelets, serum, plasma, feces, red blood cells, white blood cells or leukocytes, endothelial cells, tissue biopsy (e.g., biopsy from known or suspected solid tumors), cerebrospinal fluid, synovial fluid, lymphatic fluid, ascites, interstitial fluid or extracellular fluid (e.g., fluid from intercellular spaces), gingival fluid, gingival sulcus fluid, bone marrow, pleural effusion, cerebrospinal fluid, saliva, mucus, sputum, semen, sweat, urine.Sample is preferably body fluid, especially blood and its fraction, and urine.Such sample includes nucleic acid falling off from tumor.Nucleic acid can include DNA and RNA and can be double-stranded and single-stranded forms.Sample can be in the form of being initially separated from a subject, or can have been subjected to additional processing to remove or add components, such as cells, enrich a component relative to another component, or convert a form of nucleic acid into another, such as RNA to DNA, or single-stranded nucleic acid to double-stranded. Thus, for example, the body fluid used for analysis is plasma or serum containing cell-free nucleic acids, such as cell-free DNA (cfDNA).

[0221] In some embodiments, the volume of the body fluid sample taken from the subject depends on the expected depth of reads for the region sequenced. Exemplary volumes are about 0.4ml-40ml, about 5ml-20ml, about 10ml-20ml. For example, the volume can be about 0.5ml, about 1ml, about 5ml, about 10ml, about 20ml, about 30ml, about 40ml or more milliliters. The volume of the sampled plasma is usually between about 5ml and about 20ml.

[0222] Samples may contain varying amounts of nucleic acid. Typically, the amount of nucleic acid in a given sample is equal to more than one genome equivalent. For example, a sample of about 30 ng DNA may contain about 10,000 (10 4 ) haploid human genome equivalents, and in the case of cfDNA, contains approximately 200 billion (2x10 11) individual polynucleotide molecules. Similarly, a sample of about 100 ng of DNA may contain about 30,000 haploid human genome equivalents, and in the case of cfDNA, about 600 billion individual molecules.

[0223] In some embodiments, the sample comprises nucleic acids from different sources, such as nucleic acids from cells and from cell-free sources (e.g., blood samples, etc.). Typically, the sample includes nucleic acids carrying mutations. For example, the sample optionally includes DNA carrying germline mutations and / or somatic mutations. Typically, the sample includes DNA carrying cancer-associated mutations (e.g., cancer-associated somatic mutations).

[0224] Exemplary amounts of cell-free nucleic acid in a sample prior to amplification are generally in the range of from about 1 femtogram (fg) to about 1 microgram (μg), such as about 1 picogram (pg) to about 200 nanograms (ng), about 1 ng to about 100 ng, about 10 ng to about 1000 ng. In some embodiments, the sample comprises up to about 600 ng, up to about 500 ng, up to about 400 ng, up to about 300 ng, up to about 200 ng, up to about 100 ng, up to about 50 ng, or up to about 20 ng of cell-free nucleic acid molecules. Optionally, the amount is at least about 1 fg, at least about 10 fg, at least about 100 fg, at least about 1 pg, at least about 10 pg, at least about 100 pg, at least about 1 ng, at least about 10 ng, at least about 100 ng, at least about 150 ng, or at least about 200 ng of cell-free nucleic acid molecules. In certain embodiments, the amount is up to about 1 fg, about 10 fg, about 100 fg, about 1 pg, about 10 pg, about 100 pg, about 1 ng, about 10 ng, about 100 ng, about 150 ng, or about 200 ng of cell-free nucleic acid molecules. In some embodiments, the method comprises obtaining between about 1 fg and about 200 ng of cell-free nucleic acid molecules from a sample.

[0225] The cell-free nucleic acids typically have a size distribution between about 100 nucleotides in length and about 500 nucleotides in length, with molecules from about 110 nucleotides in length to about 230 nucleotides in length representing about 90% of the molecules in the sample, a mode at about 168 nucleotides in length, and a second, minor peak ranging between about 240 and about 440 nucleotides in length. In certain embodiments, the cell-free nucleic acids are about 160 to about 180 nucleotides in length, or about 320 to about 360 nucleotides in length, or about 440 to about 480 nucleotides in length.

[0226] In some embodiments, cell-free nucleic acid is separated from body fluid by a partitioning step, in which the cell-free nucleic acid such as present in the solution is separated from intact cells and other insoluble components of the body fluid. In some of these embodiments, distribution includes techniques such as centrifugation or filtration. Optionally, the cells in the body fluid are lysed, and cell-free nucleic acid and cell nucleic acid are processed together. Typically, after adding buffer and washing steps, cell-free nucleic acid is precipitated with, for example, ethanol. In certain embodiments, additional purification steps are used, such as silica-based columns to remove pollutants or salts. For example, non-specific bulk carrier nucleic acid is optionally added throughout the reaction to optimize certain aspects of the exemplary procedure, such as yield. After such processing, the sample typically includes various forms of nucleic acid, including double-stranded DNA, single-stranded DNA and / or single-stranded RNA. Optionally, single-stranded DNA and / or single-stranded RNA are converted into double-stranded forms, so they are included in subsequent processing and analysis steps.

[0227] Nucleic acid labeling

[0228] In some embodiments, nucleic acid molecules (from polynucleotide samples) can be tagged with sample indexes and / or molecular barcodes (commonly referred to as "tags"). Tags can be incorporated or otherwise connected to a connector by methods such as chemical synthesis, connection (e.g., blunt end connection or sticky end connection) or overlap extension polymerase chain reaction (PCR). Such adapters can ultimately be connected to target nucleic acid molecules. In other embodiments, one or more rounds of amplification cycles (e.g., PCR amplification) are typically applied to introduce sample indexes into nucleic acid molecules using conventional nucleic acid amplification methods. Amplification can be performed in one or more reaction mixtures (e.g., more than one microwell in an array). Molecular barcodes and / or sample indexes can be introduced simultaneously, or in any order. In some embodiments, molecular barcodes and / or sample indexes are introduced before and / or after performing a sequence capture step. In some embodiments, only molecular barcodes are introduced before probe capture, and sample indexes are introduced after performing a sequence capture step. In some embodiments, both molecular barcodes and sample indexes are introduced before performing a probe-based capture step. In some embodiments, sample indexes are introduced after performing a sequence capture step. In some embodiments, the molecular barcode is incorporated into a nucleic acid molecule (e.g., cfDNA molecule) in a sample by a linker via a ligation (e.g., blunt end ligation or sticky end ligation). In some embodiments, the sample index is incorporated into a nucleic acid molecule (e.g., cfDNA molecule) in a sample by overlap extension polymerase chain reaction (PCR). Typically, sequence capture schemes include the introduction of a single-stranded nucleic acid molecule complementary to a targeted nucleic acid sequence, such as a coding sequence of a genomic region, and mutations in such a region are associated with cancer types.

[0229] In some embodiments, the label can be located at one end or both ends of the sample nucleic acid molecule. In some embodiments, the label is a predetermined or random or semi-random sequence oligonucleotide. In some embodiments, the length of the label can be less than about 500, 200, 100, 50, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 nucleotides. The label can be randomly or non-randomly connected to the sample nucleic acid.

[0230] In some embodiments, each sample is uniquely labeled with a sample index or a combination of sample indexes. In some embodiments, each nucleic acid molecule of a sample or subsample is uniquely labeled with a molecular barcode or a combination of molecular barcodes. In other embodiments, more than one molecular barcode may be used so that the molecular barcodes do not have to be unique relative to each other in the more than one barcode (e.g., non-unique molecular barcodes). In these embodiments, molecular barcodes are usually attached (e.g., by connection) to separate molecules so that the combination of molecular barcodes and sequences to which they can be attached produces a unique sequence that can be tracked separately. The detection of non-uniquely labeled molecular barcodes and endogenous sequence information (e.g., corresponding to the beginning (start) and / or end (termination) part of the sequence of the original nucleic acid molecule in the sample, the subsequence of the sequence read at one or both ends, the length of the sequence read and / or the length of the original nucleic acid molecule in the sample) generally allow a unique identity to be assigned to a specific molecule. The length or base pair number of the separate sequence reads is also optionally used to assign a unique identity to a given molecule. As described herein, fragments from a single strand of nucleic acid that have been assigned a unique identity can thereby allow for subsequent identification of fragments from the parental strand and / or the complementary strand.

[0231] In some embodiments, molecular barcodes are introduced with a set of identifiers (e.g., a combination of unique or non-unique molecular barcodes) and the expected ratio of molecules in the sample. An example format uses about 2 to about 1,000,000 different molecular barcodes, or about 5 to about 150 different molecular barcodes, or about 20 to about 50 different molecular barcodes, connected to both ends of the target molecule. Alternatively, about 25 to about 1,000,000 different molecular barcodes can be used. For example, 20-50x 20-50 molecular barcodes can be used. Such a number of identifiers is sufficient so that different molecules with the same starting point and end point have a high probability of receiving different identifier combinations (e.g., at least 94%, 99.5%, 99.99% or 99.999%). In some embodiments, about 80%, about 90%, about 95%, or about 99% of molecules have the same molecular barcode combination.

[0232] In some embodiments, the assignment of unique or non-unique molecular barcodes in a reaction is performed using methods and systems described in, for example, U.S. Patent Application Nos. 20010053519, 20030152490, and 20110160078 and U.S. Patent Nos. 6,582,908, 7,537,898, 9,598,731, and 9,902,992, each of which is hereby incorporated by reference in its entirety. Alternatively, in some embodiments, only endogenous sequence information (e.g., start and / or end position, subsequence at one or both ends of the sequence, and / or length) can be used to identify different nucleic acid molecules of a sample.

[0233] Nucleic Acid Amplification

[0234] Sample nucleic acids flanked by adapters are typically amplified by PCR and other amplification methods that use nucleic acid primers that bind to primer binding sites in adapters flanking the DNA molecule to be amplified. In some embodiments, the amplification method includes cycles of extension, denaturation, and annealing caused by thermal cycling, or can be isothermal, such as in transcription-mediated amplification. Other exemplary amplification methods that are optionally used include ligase chain reaction, strand displacement amplification, nucleic acid sequence-based amplification, and self-sustaining sequence-based replication, among others.

[0235] One or more amplification cycles are typically applied to introduce molecular barcodes and / or sample indexes into nucleic acid molecules using conventional nucleic acid amplification methods. Amplification is typically performed in one or more reaction mixtures. Molecular barcodes and sample indexes are optionally introduced simultaneously, or in any order. In some embodiments, molecular barcodes and sample indexes are introduced before and / or after performing a sequence capture step. In some embodiments, molecular barcodes are only introduced before probe capture, and sample indexes are introduced after performing a sequence capture step. In certain embodiments, molecular barcodes and sample indexes are both introduced before performing a probe-based capture step. In some embodiments, sample indexes are introduced after performing a sequence capture step. Typically, a sequence capture scheme includes introducing a single-stranded nucleic acid molecule complementary to a targeted nucleic acid sequence, such as a coding sequence of a genomic region, and mutations in such a region are associated with cancer types. Typically, the amplification reaction produces more than one non-unique or uniquely tagged nucleic acid amplicon having a molecular barcode and a sample index ranging in size from about 200 nucleotides (nt) to about 700 nt, from 250 nt to about 350 nt, or from about 320 nt to about 550 nt. In some embodiments, the amplicon has a size of about 300 nt. In some embodiments, the amplicon has a size of about 500 nt.

[0236] Nucleic acid enrichment

[0237] In some embodiments, the sequence is enriched before nucleic acid sequencing. Enrichment is optionally carried out for a specific target region or non-specifically ("target sequence"). In some embodiments, the target region of interest can be enriched using differential tiling and capture schemes (differential tiling and capture schemes) with nucleic acid capture probes ("baits") selected for one or more bait sets. Differential tiling and capture schemes typically use different relative concentrations of bait sets to differentially tile (e.g., with different "resolutions") in the genomic region associated with the bait, subject to a set of restrictions (e.g., sequencer restrictions, such as sequencing loading, the utility of each bait, etc.), and capture the targeted nucleic acid at the level required for downstream sequencing. These targeted genomic regions of interest optionally include natural or synthetic nucleotide sequences of nucleic acid constructs. In some embodiments, biotin-labeled beads with probes for one or more regions of interest can be used to capture target sequences, and optionally subsequently amplify these regions to enrich the region of interest.

[0238] Sequence capture generally includes the use of oligonucleotide probes that hybridize with the target nucleic acid sequence. In certain embodiments, the probe set strategy includes tiling the probe in the region of interest. The length of such probe can be, for example, from about 60 to about 120 nucleotides. The set can have a depth of about 2x, 3x, 4x, 5x, 6x, 8x, 9x, 10x, 15x, 20x, 50x or greater. The effectiveness of sequence capture generally depends in part on the length of the sequence complementary (or almost complementary) to the probe sequence in the target molecule.

[0239] Nucleic acid sequencing

[0240] In the case of carrying out or not carrying out previous amplification, the sample nucleic acid optionally flanked by adapters is generally subjected to sequencing.Sequencing methods or optionally commercially available formats include, for example, Sanger sequencing, high-throughput sequencing, pyrophosphate sequencing, synthetic sequencing, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, connection sequencing, hybridization sequencing, RNA-Seq (Illumina), digital gene expression (Helicos), next generation sequencing (NGS), single-molecule synthetic sequencing (SMSS) (Helicos), massive parallel sequencing, cloned single-molecule array (Solexa), shotgun sequencing, Ion Torrent, Oxford Nanopore, Roche Genia, Maxim-Gilbert sequencing, primer walking, sequencing using PacBio, SOLiD, Ion Torrent or Nanopore platforms.Sequencing reaction can be carried out in various sample processing units, and the sample processing unit may include multi-channel, multi-channel, multi-porous or other devices that substantially process more than one sample set simultaneously.The sample processing unit may also include more than one sample chamber so that more than one operation can be processed simultaneously.

[0241] Sequencing reaction can be carried out to one or more nucleic acid fragment types or regions of the markers known to contain cancer or other diseases. Sequencing reaction can also be carried out to any nucleic acid fragment present in the sample. Sequencing reaction can be carried out to at least about 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9% or 100% of the genome. In other cases, sequencing reaction can be carried out to less than about 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9% or 100% of the genome.

[0242] The simultaneous sequencing reactions can be performed using multiple sequencing techniques. In some embodiments, the cell-free polynucleotides are sequenced using at least about 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000 or 100,000 sequencing reactions. In other embodiments, the cell-free polynucleotides are sequenced using less than about 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000 or 100,000 sequencing reactions. The sequencing reactions are typically performed sequentially or simultaneously. Subsequent data analysis is typically performed on all or part of the sequencing reactions. In some embodiments, data analysis is performed on at least about 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, or 100,000 sequencing reactions. In other embodiments, data analysis is performed on less than about 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, or 100,000 sequencing reactions. Exemplary read depths are about 1000 to about 50,000 reads per site (base position).

[0243] In some embodiments, nucleic acid colonies are prepared for sequencing by enzymatic formation of flat ends on double-stranded nucleic acids with single-stranded overhangs at one or both ends. In these embodiments, the colony is usually processed with an enzyme with 5'-3' DNA polymerase activity and 3'-5' exonuclease activity in the presence of nucleotides (e.g., A, C, G and T or U). The exemplary enzyme or its catalytic fragment optionally used includes Klenow large fragment and T4 polymerase. At the 5' overhang, the enzyme usually extends the 3' end recessed on the relative chain until it is flush with the 5' end, producing a flat end. At the 3' overhang, the enzyme usually digests from the 3' end until the 5' end of the relative chain, and sometimes exceeds the 5' end. If this digestion proceeds to the 5' end exceeding the relative chain, the vacancy can be filled with an enzyme with the same polymerase activity as for the 5' overhang. The formation of flat ends on the double-stranded nucleic acid is conducive to the attachment of, for example, adapters and subsequent amplification.

[0244] In some embodiments, the nucleic acid population is subjected to additional processing, such as conversion of single-stranded nucleic acid to double-stranded and / or conversion of RNA to DNA. These forms of nucleic acid are also optionally connected to adapters and amplified.

[0245] With or without prior amplification, the nucleic acid undergoing the above-described process of forming a blunt end and optionally other nucleic acids in the sample can be sequenced to produce sequenced nucleic acids. Sequenced nucleic acids can refer to the sequence (i.e., sequence information) of a nucleic acid or a nucleic acid whose sequence has been determined. Sequencing can be performed to provide sequence data for a single nucleic acid molecule in a sample directly or indirectly from the consensus sequence of the amplification products of the single nucleic acid molecules in the sample.

[0246] In some embodiments, after the flat ends are formed, the double-stranded nucleic acids in the sample with single-stranded protrusions are connected to adapters including molecular barcodes at both ends, and sequencing is performed to determine the nucleic acid sequence and the molecular barcode introduced by the adapter. The flat-ended DNA molecules are optionally connected to the flat ends of at least partially double-stranded adapters (e.g., Y-shaped adapters or bell-shaped adapters). Optionally, the flat ends of the sample nucleic acids and adapters can be tailed with complementary nucleotides to facilitate connection (for example, sticky end connection).

[0247] The nucleic acid sample is usually contacted with a sufficient number of adapters so that the probability that any two copies of the same nucleic acid receive the same combination of adapter barcodes (i.e., molecular barcodes) from the adapters connected to the two ends is low (e.g., <1 or 0.1%). Using adapters in this way allows the identification of a family of nucleic acid sequences having the same starting and ending points on the reference nucleic acid and connected to the same combination of molecular barcodes. Such a family represents the sequence of the amplification product of the nucleic acid in the sample before amplification. Through blunt end formation and adapter attachment modification, the sequence of the family members can be compiled to obtain one or more common nucleotides or a complete common sequence of the nucleic acid molecule in the original sample. In other words, the nucleotides occupying the specified position of the nucleic acid in the sample are determined to occupy the common nucleotides occupying the corresponding position in the family member sequence. The family can include the sequence of one or two chains of a double-stranded nucleic acid. If the members of the family include sequences from two chains of a double-stranded nucleic acid, the sequence of one chain is converted into their complementary sequences for the purpose of compiling all sequences to obtain one or more common nucleotides or sequences. Some families include only a single member sequence. In this case, the sequence can be obtained as the sequence of the nucleic acid in the sample before amplification. Alternatively, families with only a single member sequence can be eliminated from subsequent analysis.

[0248] Nucleotide variation in the nucleic acid of sequencing can be determined by comparing the nucleic acid of sequencing with a reference sequence. A reference sequence is a known sequence typically, for example, a known full genome or partial genome sequence (for example, a full genome sequence of a human subject) from a subject. A reference sequence can be, for example, hG19 or hG38. As described above, the nucleic acid of sequencing can represent the directly determined sequence of the nucleic acid in the sample, or the consensus sequence of the amplification product of such nucleic acid. Comparisons can be made at one or more specified positions on the reference sequence. When the corresponding sequence is aligned to the greatest extent, a subset of the nucleic acid of sequencing can be identified, including a position corresponding to the specified position of the reference sequence. In such a subset, it can be determined which (if any) nucleic acid of sequencing includes nucleotide variation at a specified position, and optionally which (if any) include reference nucleotides (that is, the same as in the reference sequence). If the number of nucleic acids of sequencing including nucleotide variants in the subset exceeds the threshold value selected, variant nucleotides can be called at a specified position. The threshold value can be a simple numerical value, such as at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 nucleic acids sequenced in the subset comprising nucleotide variants, or it can be a ratio, such as at least 0.5, 1, 2, 3, 4, 5, 10, 15 or 20 nucleic acids sequenced in the subset comprising nucleotide variants, and other possibilities. Any position of interest in the reference sequence can be repeated for comparison. Sometimes it is possible to compare the positions of the appointments occupying at least about 20, 100, 200 or 300 continuous positions on the reference sequence, for example, about 20-500 or about 50-300 continuous positions.

[0249] Additional details about nucleic acid sequencing, including the formats and applications described herein, are also provided, for example, in Levy et al., Annual Review of Genomics and Human Genetics, 17:95-115 (2016), Liu et al., J. of Biomedicine and Biotechnology, Volume 2012, Article ID 251364:1-11 (2012), Voelkerding et al., Clinical Chem., 55:641-658 (2009), MacLean et al., Nature Rev. Microbiol., 7:287-296 (2009), Astier et al., J Am Chem. Soc., 128(5):1705-10(2006), U.S. Patent No. 6,210,891, U.S. Patent No. 6,258,568, U.S. Patent No. 6,833,246, U.S. Patent No. 7,115,400, U.S. Patent No. 6,969,488, U.S. Patent No. 5,912,148, U.S. Patent No. 6,130,073, U.S. Patent No. 7,169,560, U.S. Patent No. 7,282,337, U.S. U.S. Patent No. 7,482,120, U.S. Patent No. 7,501,245, U.S. Patent No. 6,818,395, U.S. Patent No. 6,911,345, U.S. Patent No. 7,501,245, U.S. Patent No. 7,329,492, U.S. Patent No. 7,170,050, U.S. Patent No. 7,302,146, U.S. Patent No. 7,313,308, and U.S. Patent No. 7,476,503, all of which are incorporated by reference in their entirety.

[0250] Comparator Result

[0251] Tumor mutation burden (TMB) of the adjustment of a given subject determined according to the method disclosed in the present application, is usually compared with the database of the comparator result (e.g., TMB) from a reference population, to identify customized or targeted therapy for the subject. In some embodiments, the TMB of the test subject and the comparator TMB are measured in, for example, the whole genome or the whole exon group, and in other embodiments, these TMBs are measured based on, for example, a subset or a targeted region of a genome or an exon group, and these subsets or targeted regions are optionally extrapolated to determine the TMB of the whole genome or the whole exon group. Generally, the reference population includes patients with the same cancer type as the test subject and / or patients receiving or having received the same treatment as the test subject. In some embodiments, the test subject TMB and the comparator TMB are measured by determining a predetermined or selected set of genes or mutation counts or loads in a genomic region. It is optionally selected that substantially any gene (e.g., oncogene) is used for this analysis. In some of these embodiments, the selected genes or genomic regions include at least about 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1,500, 2,000 or more selected genes or genomic regions. In some of these embodiments, the selected genes or genomic regions optionally include one or more genes listed in Table 1.

[0252] Table 1

[0253]

[0254] In certain embodiments, the selected genes or genomic regions optionally include one or more genes listed in Table 2.

[0255] Table 2

[0256]

[0257]

[0258] cancer

[0259] In certain embodiments, the methods and systems disclosed herein are used to identify customized treatments for a given disease or condition in a patient. Typically, the disease under consideration is a cancer. Non-limiting examples of such cancers include biliary tract cancer, bladder cancer, transitional cell carcinoma, urothelial carcinoma, brain cancer, glioma, astrocytoma, breast cancer, metaplastic carcinoma, cervical cancer, cervical squamous cell carcinoma, rectal cancer, colorectal cancer, colon cancer, hereditary non-polyposis colorectal cancer, colorectal adenocarcinoma, gastrointestinal stromal tumor (GIST), endometrial cancer, endometrial stromal sarcoma, esophageal cancer, esophageal squamous cell carcinoma, esophageal adenocarcinoma, ocular melanoma, uveal melanoma, gallbladder cancer, gallbladder adenocarcinoma, renal cell carcinoma, clear cell renal cell carcinoma, transitional cell carcinoma, urothelial carcinoma, Wilms' tumor, leukemia, acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), chronic myelomonocytic leukemia (CMML), liver cancer, hepatoepithelial carcinoma, hepatocellular carcinoma, cholangiocarcinoma, hepatoblastoma, lung cancer, non-small cell lung cancer (NSCLC), mesothelioma, B-cell lymphoma, non-Hodgkin's lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, T-cell lymphoma, non-Hodgkin's lymphoma, precursor T-lymphoblastic lymphoma / leukemia, peripheral T-cell lymphoma, multiple myeloma, nasopharyngeal carcinoma (NPC), neuroblastoma, oral cancer, oral squamous cell carcinoma, osteosarcoma, ovarian cancer, pancreatic cancer, pancreatic ductal adenocarcinoma, pseudopapillary tumor, acinar cell carcinoma, prostate cancer, prostate adenocarcinoma, skin cancer, melanoma, malignant melanoma, cutaneous melanoma, small intestine cancer, stomach cancer, gastric epithelial carcinoma, gastrointestinal stromal tumor (GIST), uterine cancer or uterine sarcoma.

[0260] Customized treatment and related administration

[0261] In some embodiments, methods disclosed herein relate to identifying and administering customized treatment to patients with a given adjusted TMB. Substantially any cancer treatment (e.g., surgical treatment, radiotherapy, chemotherapy, etc.) is included as part of these methods. Typically, customized treatment includes at least one immunotherapy (or immunotherapeutic agent). Immunotherapy generally refers to a method for enhancing the immune response for a given cancer type. In certain embodiments, immunotherapy refers to a method for enhancing the T cell response for a tumor or cancer.

[0262] In some embodiments, immunotherapy or immunotherapeutic agents target immune checkpoint molecules. Certain tumors are able to evade the immune system by co-opting immune checkpoint pathways. Therefore, targeting immune checkpoints has become an effective method to combat the ability of tumors to evade the immune system and activate anti-tumor immunity against certain cancers. Pardoll, Nature Reviews Cancer, 2012, 12: 252-264.

[0263] In certain embodiments, immune checkpoint molecules are inhibitory molecules that reduce the signals involved in the response of T cells to antigens. For example, CTLA4 is expressed on T cells and plays a role in downregulating T cell activation by binding to CD80 (also known as B7.1) or CD86 (also known as B7.2) on antigen presenting cells. PD-1 is another inhibitory checkpoint molecule expressed on T cells. PD-1 limits the activity of T cells in peripheral tissues during inflammatory responses. In addition, PD-1 ligands (PD-L1 or PD-L2) are usually upregulated on the surface of many different tumors, resulting in downregulation of anti-tumor immune responses in tumor microenvironments. In certain embodiments, inhibitory immune checkpoint molecules are CTLA4 or PD-1. In other embodiments, inhibitory immune checkpoint molecules are ligands of PD-1, such as PD-L1 or PD-L2. In other embodiments, inhibitory immune checkpoint molecules are ligands of CTLA4, such as CD80 or CD86. In other embodiments, the inhibitory immune checkpoint molecule is lymphocyte activation gene 3 (LAG3), killer cell immunoglobulin-like receptor (KIR), T-cell membrane protein 3 (TIM3), galectin 9 (GAL9), or adenosine A2a receptor (A2aR).

[0264] Antagonists targeting these immune checkpoint molecules can be used to enhance antigen-specific T cell responses against certain cancers. Therefore, in certain embodiments, immunotherapy or immunotherapeutic agents are antagonists of inhibitory immune checkpoint molecules. In certain embodiments, the inhibitory immune checkpoint molecule is PD-1. In certain embodiments, the inhibitory immune checkpoint molecule is PD-L1. In certain embodiments, the antagonist of the inhibitory immune checkpoint molecule is an antibody (e.g., a monoclonal antibody). In certain embodiments, the antibody or monoclonal antibody is an anti-CTLA4, anti-PD-1, anti-PD-L1, or anti-PD-L2 antibody. In certain embodiments, the antibody is a monoclonal anti-PD-1 antibody. In certain embodiments, the antibody is a monoclonal anti-PD-L1 antibody. In certain embodiments, the monoclonal antibody is a combination of an anti-CTLA4 antibody and an anti-PD-1 antibody, an anti-CTLA4 antibody and an anti-PD-L1 antibody, or an anti-PD-L1 antibody and an anti-PD-1 antibody. In certain embodiments, the anti-PD-1 antibody is pembrolizumab. Nivolumab In certain embodiments, the anti-CTLA4 antibody is ipilimumab. In certain embodiments, the anti-PD-L1 antibody is atezolizumab , avelumab or durvalumab One or more of .

[0265] In certain embodiments, immunotherapy or immunotherapeutic agent is an antagonist (eg, antibody) for CD80, CD86, LAG3, KIR, TIM3, GAL9, TIGIT or A2aR. In other embodiments, antagonists are soluble forms of inhibitory immune checkpoint molecules, such as soluble fusion proteins comprising the extracellular domain of inhibitory immune checkpoint molecules and the Fc domain of antibodies. In certain embodiments, soluble fusion proteins include the extracellular domain of CTLA4, PD-1, PD-L1 or PD-L2. In some embodiments, soluble fusion proteins include the extracellular domain of CD80, CD86, LAG3, KIR, TIM3, GAL9 or A2aR. In one embodiment, soluble fusion proteins include the extracellular domain of PD-L2 or LAG3.

[0266] In certain embodiments, immune checkpoint molecules are co-stimulatory molecules that amplify the signals involved in the response of T cells to antigens. For example, CD28 is a co-stimulatory receptor expressed on T cells. When T cells bind to antigens through their T cell receptors, CD28 binds to CD80 (also known as B7.1) or CD86 (also known as B7.2) on antigen presenting cells to amplify T cell receptor signaling and promote T cell activation. Because CD28 binds to the same ligand (CD80 and CD86) as CTLA4, CTLA4 can offset or regulate the co-stimulatory signaling mediated by CD28. In certain embodiments, immune checkpoint molecules are co-stimulatory molecules selected from CD28, inducible T cell co-stimulatory factors (ICOS), CD137, OX40 or CD27. In other embodiments, immune checkpoint molecules are ligands including co-stimulatory molecules such as CD80, CD86, B7RP1, B7-H3, B7-H4, CD137L, OX40L or CD70.

[0267] Agonists targeting these co-stimulatory checkpoint molecules can be used to enhance antigen-specific T cell responses for certain cancers. Therefore, in certain embodiments, immunotherapy or immunotherapeutic agents are agonists of co-stimulatory checkpoint molecules. In certain embodiments, the agonist of co-stimulatory checkpoint molecules is an agonist antibody, and preferably a monoclonal antibody. In certain embodiments, the agonist antibody or monoclonal antibody is an anti-CD28 antibody. In other embodiments, the agonist antibody or monoclonal antibody is an anti-ICOS, anti-CD137, anti-OX40 or anti-CD27 antibody. In other embodiments, the agonist antibody or monoclonal antibody is an anti-CD80, anti-CD86, anti-B7RP1, anti-B7-H3, anti-B7-H4, anti-CD137L, anti-OX40L or anti-CD70 antibody.

[0268] In certain embodiments, the customized treatment described herein is usually administered parenterally (e.g., intravenously or subcutaneously). Pharmaceutical compositions containing immunotherapeutics are usually administered intravenously. Certain therapeutic agents are administered orally. However, customized treatments (e.g., immunotherapeutics, etc.) can also be administered by any method known in the art, including, for example, oral administration, sublingual administration, rectal administration, vaginal administration, intraurethral administration, topical administration, intraocular administration, intranasal administration, and / or intraatrial administration, which can include tablets, capsules, granules, aqueous suspensions, gels, sprays, suppositories, salves, ointments, etc.

[0269] System and computer readable medium

[0270] The present disclosure also provides various systems and computer program products or machine-readable media. For example, in some embodiments, systems, distributed computing hardware and applications (e.g., cloud computing services), electronic communication networks, communication interfaces, computer program products, machine-readable media, electronic storage media, software (e.g., machine executable code or logic instructions), etc. are optionally used to perform or facilitate the methods described herein. For illustration, Figure 2Provide a schematic diagram of an exemplary system suitable for implementing at least some aspects of the method disclosed in the present application. As shown, system 200 includes at least one controller or computer, such as server 202 (such as search engine server), which includes processor 204 and memory, storage device or memory component 206, and is located away from remote server 202, and communicates with remote server 202 through electronic communication network 212 (such as the Internet or other interconnected networks) One or more other communication devices 214 and 216 (such as client computer terminal, phone, tablet computer, notebook computer, other mobile devices, etc.). Communication device 214 and 216 generally include an electronic display (such as a computer supporting the Internet, etc.) that communicates with, for example, server 202 computers through network 212, wherein the electronic display includes a user interface (such as a graphical user interface (GUI), a network-based user interface, etc.) for displaying results when implementing the method described herein. In certain embodiments, the communication network also includes, for example, using a hard drive, a thumb drive or other data storage mechanism to physically transfer data from one location to another. The system 200 also includes a program product 208 stored on a computer or machine readable medium, such as, for example, one or more memories of various types, such as memory 206 of server 202, which can be read by server 202 to facilitate, for example, directing a search application or other applications that can be executed by one or more other communication devices such as 214 (schematically shown as a desktop or personal computer) and 216 (schematically shown as a tablet computer). In some embodiments, the system 200 optionally also includes at least one database server, such as, for example, a server 210 associated with an online website, which has data stored thereon (e.g., control sample or comparator result data, indexed customized treatments, etc.) that can be searched directly or through a search engine server 202. The system 200 optionally also includes one or more other servers located remotely from the server 202, each server optionally associated with one or more database servers 210 located remotely from or local to each other server. The other servers can be beneficial in providing services to geographically remote users and enhancing geographically distributed operations.

[0271] As will be appreciated by those skilled in the art, the memory 206 of the server 202 optionally includes volatile and / or non-volatile memory, including, for example, RAM, ROM, and magnetic or optical disks, etc. It will also be appreciated by those skilled in the art that, although shown as a single server, the configuration of the server 202 shown is given only as an example, and other types of servers or computers configured according to various other methods or architectures may also be used. Figure 2Schematically shown server 202 represents a server or server cluster (server cluster) or server farm (server farm), and is not limited to any single physical server. The server site can be deployed as a server farm or server cluster managed by a server hosting provider. The number of servers and their architecture and configuration can be increased based on the use, demand and capacity requirements of system 200. As also understood by those of ordinary skill in the art, other user communication devices 214 and 216 in these embodiments can be, for example, notebook computers, desktop computers, tablet computers, personal digital assistants (PDA), mobile phones, servers or other types of computers. As known and understood by those of ordinary skill in the art, network 212 can include the Internet, intranet, telecommunication network, extranet or more than one computer / server's World Wide Web, which communicate with one or more other computers through a communication network, and / or a local network or other local area network's part.

[0272] As further understood by those of ordinary skill in the art, the exemplary program product or machine-readable medium 208 is optionally in the form of microcode, a program, a cloud computing format, routines, and / or a symbolic language that provides one or more sets of ordered operations that control the functionality of the hardware and direct its operation. According to an exemplary embodiment, the program product 208 also need not reside entirely in volatile memory, but can be selectively loaded as needed according to various methods known and understood by those of ordinary skill in the art.

[0273] As further understood by those of ordinary skill in the art, the term "computer-readable medium" or "machine-readable medium" refers to any medium that participates in providing instructions to a processor for execution. For illustration, the term "computer-readable medium" or "machine-readable medium" includes distribution media, cloud computing formats, intermediate storage media, execution memory of a computer, and any other medium or device capable of storing a program product 608 that implements the functions or processes of various embodiments of the present disclosure, for example, for reading by a computer. "Computer-readable medium" or "machine-readable medium" can take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical disks or magnetic disks. Volatile media include dynamic memory, such as the main memory of a given system. Transmission media include coaxial cables, copper wires, and optical fibers, including wires that make up a bus. Transmission media can also take the form of sound waves or light waves, such as those generated during radio wave and infrared data communications. Example forms of computer readable media include floppy disks, flexible disks, hard disks, magnetic tapes, flash drives or any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tapes, any other physical storage media with patterns of holes, RAM, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or cartridges, carrier waves, or any other medium from which a computer can read.

[0274] The program product 208 is optionally copied from a computer readable medium to a hard disk or similar intermediate storage medium. When the program product 208 or a portion thereof is to be run, it is optionally loaded from their distribution medium, their intermediate storage medium, etc. into the execution memory of one or more computers, configuring the computer to operate according to the functions or methods of various embodiments. All such operations are well known to those of ordinary skill in the art of computer systems, for example.

[0275] For further explanation, in certain embodiments, the present application provides a system including one or more processors and one or more memory components in communication with the processor. The memory component typically includes one or more instructions that, when executed, cause the processor to provide information that causes at least one mutation count, adjusted result / TMB, comparator result, customized treatment, etc. to be displayed (e.g., via communication devices 214, 216, etc.) and / or receive information from other system components and / or from a system user (e.g., via communication devices 214, 216, etc.).

[0276] In some embodiments, the program product 208 includes non-transitory computer-executable instructions that, when executed by the electronic processor 204, at least: (i) determine an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from a subject, (ii) determine a tumor fraction and / or coverage of the nucleic acid to generate sequencing parameters, (iii) determine an expected mutation score and / or an expected distribution of expected mutation scores given the sequencing parameters to generate an expected outcome, (iv) adjust the observed mutation count given the expected outcome to generate an adjusted outcome to detect a tumor mutation burden (TMB) in the subject; and optionally (v) compare the adjusted outcome to one or more comparator outcomes, wherein a substantial match between the adjusted outcome and the comparator outcome indicates a predicted response of the subject to treatment.

[0277] The system 200 also typically includes additional system components configured to perform various aspects of the methods described herein. In some of these embodiments, one or more of these additional system components are remote from the remote server 202 and communicate with the remote server 202 via an electronic communication network 212, while in other embodiments, one or more of these additional system components are located locally and communicate with the server 202 (i.e., without the electronic communication network 212), or directly communicate with, for example, a desktop computer 214.

[0278] In some embodiments, for example, additional system components including a sample preparation component 218 are operably connected (directly or indirectly (e.g., via an electronic communication network 212)) to the controller 202. The sample preparation component 218 is configured to prepare nucleic acids in the sample (e.g., prepare a nucleic acid library) for amplification and / or sequencing by a nucleic acid amplification component (e.g., a thermal cycler, etc.) and / or a nucleic acid sequencer. In some of these embodiments, the sample preparation component 218 is configured to separate nucleic acids from other components in the sample, attach one or more adapters comprising a molecular barcode to the nucleic acid described herein, selectively enrich one or more regions from the genome or transcriptome prior to sequencing, and the like.

[0279] In certain embodiments, system 200 also includes a nucleic acid amplification component 220 (e.g., a thermal cycler, etc.) operably connected (directly or indirectly (e.g., via electronic communication network 212)) to controller 202. Nucleic acid amplification component 220 is configured to amplify nucleic acids in a sample from a subject. For example, nucleic acid amplification component 220 is optionally configured to amplify a region selectively enriched from a genome or transcriptome in a sample as described herein.

[0280] The system 200 typically also includes at least one nucleic acid sequencer 222, which is operably connected (directly or indirectly (e.g., via an electronic communication network 212)) to the controller 202. The nucleic acid sequencer 222 is configured to provide sequence information of nucleic acids (e.g., amplified nucleic acids) from a subject sample. Essentially any type of nucleic acid sequencer can be applicable to these systems. For example, the nucleic acid sequencer 222 is optionally configured to perform pyrophosphate sequencing, single molecule sequencing, nanopore sequencing, semiconductor sequencing, synthetic sequencing, ligation sequencing, hybridization sequencing, or other techniques on nucleic acids to generate sequencing reads. Optionally, the nucleic acid sequencer 222 is configured to group sequence reads into sequence read families, each family including sequence reads generated from nucleic acids in a given sample. In some embodiments, the nucleic acid sequencer 222 generates sequencing reads using a clonal single molecule array derived from a sequencing library. In certain embodiments, the nucleic acid sequencer 222 includes at least one chip having a micropore array for sequencing a sequencing library to generate sequencing reads.

[0281] To facilitate full or partial system automation, the system 200 typically also includes a material transfer component 224 that is operably connected (directly or indirectly (e.g., via the electronic communication network 212)) to the controller 202. The material transfer component 224 is configured to transfer one or more materials (e.g., nucleic acid samples, amplicons, reagents, etc.) to and / or from the nucleic acid sequencer 222, the sample preparation component 218, and the nucleic acid amplification component 220.

[0282] Additional details related to computer systems and networks, databases, and computer program products are also provided in, for example, Peterson, Computer Networks: A Systems Approach, Morgan Kaufmann, 5th Ed. (2011), Kurose, Computer Networking: A Top-Down Approach, Pearson, 7 th Ed. (2016), Elmasri, Fundamentals of Database Systems, Addison Wesley, 6th Ed. (2010), Coronel, Database Systems: Design, Implementation, & Management, Cengage Learning, 11 thEd. (2014), Tucker, Programming Languages, McGraw-Hill Science / Engineering / Math, 2nd Ed. (2006), and Rhoton, Cloud Computing Architected: Solution Design Handbook, Recursive Press (2011), all of which are incorporated by reference in their entireties.

[0283] Immune library sequencing

[0284] In addition to the TMB analysis described herein, the present application also provides a method for sequencing the immune repertoire. Immune receptors (T cell receptors (TCR) of T cells, immunoglobulins (Ig) of B cells) are these lymphocytes (more than 10 in a healthy human body). 6 When the immune system recognizes its pathogens (i.e., new antigens of vaccines, pathogens, and cancer mutations), it triggers an immune response, allowing antigen-specific T cells to expand into clones. By sequencing the uniqueness of immune receptors such as TCR, it allows tracking of immune responses in patient samples. In certain embodiments, analysis of the immune spectrum library allows for complementary analysis of TMB analysis. Although higher TMB scores may be associated with a patient's response to immunotherapy (such as anti-PD1, anti-PDL1, anti-CTLA4), measuring the immune spectrum library is an actual functional result in response to the higher incidence of new antigens present in cancer. In addition, immune spectrum library sequencing allows the identification of blood cancers because some, such as leukemia cells and other cancer cells, can have different receptor clonal characteristics. By tracking the receptors of the original cancer cells, this can also be extended to minimal residual disease (MRD) studies of blood cancers.

[0285] To further illustrate, Figure 3A flow chart is provided, which schematically depicts an exemplary method step of combining cell-free nucleic acid sequencing (with TMB analysis) with immune spectrum library sequencing from the same sample according to some embodiments of the present disclosure. As shown in the figure, method 300 includes obtaining a test sample (e.g., a blood sample) from a subject. The test sample undergoes a separation or extraction step to produce a plasma fraction containing cell-free nucleic acid and a buffy coat fraction containing lymphocytes. In the described embodiment, the ctDNA in the plasma sample portion undergoes an extraction step based on ProK as part of the process of identifying a mutation or variation in ctDNA. Optionally, the method includes a targeting group and a methyl binding domain (MBD) allocation step. For example, additional details of epigenetic modification analysis optionally applicable to the method disclosed herein are described in WO 2018 / 119452 filed on December 22, 2017, which is incorporated by reference. As shown in the figure, ctDNA analysis also includes various library preparations (end repair, connection, polymerase chain reaction (PCR) amplification, etc.) and enrichment steps. Method 300 includes a TCR clonal typing assay, which includes a genomic DNA (gDNA) extraction step, a library preparation step (e.g., TCR barcoding, cleanup, multiplex nested PCR, and further cleanup and quantification steps). As also shown, the exemplary method 300 includes, optionally in addition to or in lieu of the TCR clonal typing assay, an immune receptor discovery assay / blood cancer detection and a minimal residual disease (MRD) assay, which includes RNA extraction, reverse transcription and template switching, and cleanup steps and various PCR and cleanup steps. The products of the various assays of method 300 are pooled, sequenced, and further analyzed by the bioinformatics pipeline (BIP) described herein. A further description of the sample preparation method, including the PCR step, is shown in Figure 4A and Figure 4B in, for TCR clonal typing assay and immune receptor discovery assay, respectively.

[0286] To further illustrate, Figure 5Schematically depicted is a method 500, which includes analysis of the buffy coat and plasma portion of a blood sample. As shown in the figure, the buffy coat analysis includes evaluating the immune spectrum library and other immune genes of clonal hematopoiesis (e.g., "clonal hematopoiesis of uncertain potential" or "CHIP") mutations, which are further evaluated by the bioinformatics pipeline described herein. Determination includes evaluating T cell receptor (TCR) and B cell receptor (BCR) clonotypes and evaluating clonal expansion or new antigen receptors. As for the plasma portion of the sample, the ctDNA library is evaluated using the bioinformatics pipeline described herein, including CHIP related information obtained from the buffy coat sample fraction of the sample. As part of the evaluation of the plasma sample fraction, single nucleotide variants (SNV), microsatellite instability (MSI) and fusion are evaluated to generate TMB scores. As shown in the figure, the collected data can be used to associate mutations to identify new antigen orphan receptors between the two described assay pathways. As shown in the figure, the results of the two described assay pathways are also used to generate improved scores for identifying possible immunotherapy responses.

[0287] In some embodiments, for example, genomic DNA is isolated from the buffy coat using a standard kit (e.g., commercially available from Qiagen or other suppliers), and amplicons are then enriched using two nested PCRs, which target TCR and β subunits outside highly variable regions such as CDR3. The second of the two PCRs involves not only amplifying TCR gDNA, but also adding partial adapters for sequencing (e.g., SP5 and SP7 adapters). In this exemplary embodiment, these partial adapters are used in the final third "index" PCR to add full-length P5 and P7 oligonucleotides to the desired TCR amplicon, generating a library that can be sequenced on, for example, an Illumina sequencer. Example

[0288] Example 1: Adjusting TMB score in samples with lower tumor fraction and lower coverage

[0289] Aliquots of patient samples with known high tumor scores and high coverage were diluted 3 to 4 times with non-tumor cfDNA (i.e., normal cfDNA) to obtain samples with lower tumor scores and lower coverage. The diluted samples were processed and analyzed using a blood-based DNA assay developed by Guardant Health, Inc. (Redwood City, CA). Mutation counts were estimated from bioinformatics analysis, and the TMB correction model was applied to determine the corrected TMB score. The observed mutation counts, maximum MAF, and coverage of the diluted samples were provided as input parameters. The model estimates the expected mutation score (f), the upper limit of the 95% confidence level of the expected mutation score (f 上限), adjusted mutation counts and TMB scores. As shown in Table 3, the reported TMB score from the model of the diluted sample (23.5 mutations per megabase) is very close to the TMB score of the original high tumor score sample (23.2 mutations per megabase). Figure 6 Shown are TMB score plots for diluted samples with (o) and without (Δ) the TMB correction model.

[0290] Table 3

[0291]

[0292] Example 2: Landscape and genomic correlates of ctDNA-based tumor mutational burden in six solid tumor types

[0293] introduction

[0294] Tumor mutation burden (TMB) is a predictive biomarker for response to immune checkpoint inhibitor (ICI) treatment. The current group-based TMB algorithm aggregates signals from certain types of somatic variants (e.g., non-synonymous coding SNVs). Because many patients with high TMB do not respond to ICI, it is studied whether other variant types and other genomic correlations will improve the calculation of TMB. In addition, plasma TMB improves the yield of reportable TMB results relative to tissue, but may underestimate the TMB of low tumor DNA shedding. This embodiment uses additional genomic features and low DNA shedding adjustments, and by studying thousands of samples of more than one cancer type, using the operation on a high-sensitivity 500 gene cfDNA sequencing platform (large group determination), the TMB algorithm is improved.

[0295] method

[0296] A cfDNA-based TMB algorithm was developed that is robust to variable tumor shedding levels. cfDNA-based TMB was evaluated in over 1,000 plasma samples across six solid tumor types, including lung, colorectal, and prostate. The contribution of silent SNVs and indels to the TMB score was examined. Correlations between TMB tumor type, patient ethnicity, and lung tumor molecular subtypes were studied. Finally, the landscape of TMB and other genomic features were also investigated: subclonality, chromosomal instability, and microsatellite instability (MSI). Additional details on the large panel assay are provided in Table 4. In addition, the TMB workflow used in this example is Figure 7 Depicted schematically.

[0297] Table 4: 500-gene cfDNA sequencing platform (large panel assay)

[0298]

[0299]

[0300] result

[0301] 1. Large group assay performance

[0302] Table 5 provides a summary of the analytical validation performance and specifications for a large panel of assays based on 30 ng cfDNA input.

[0303] Table 5

[0304]

[0305] *Analytical sensitivity range depends on the clinical relevance of the targeted regions. Sensitivity outside of these regions or in highly repetitive sequence environments may vary.

[0306] **PPV per variant based on the expected number of somatic variants per sample.

[0307] Somatic / germline status is determined using a beta binomial statistical model that deviates from the local germline mutant allele fraction, which is further described in, for example, Nance et al. (2018) A novel approach to differentiate somatic vs. germline variants in liquid biopsies using a beta binomial model. AACR. Poster 4272, which is incorporated by reference. This method does not rely on a database of common germline mutations (e.g., dbSNP).

[0308] 2. Group-based components of TMB

[0309] Fig. 8A , Figure 8B and Figure 8C is a graph showing the types of mutations associated with nonsynonymous coding SNVs: In the entire cohort, ( Fig. 8A )Synonymous SNV, ( Figure 8B ) insertion and deletion and ( Figure 8C ) Intronic SNVs are correlated with non-synonymous coding SNVs (Pearson's r = 0.90, 0.71, 0.89). The variability of mutation counts (box plots) is consistent with expectations based on sample-specific mutation rates (black lines: thick: median, thin: IQR).

[0310] 3. Adjusted plasma TMB is largely independent of input

[0311] Figure 9A-9D is a graph showing the following: Tumor dropout correction eliminates the effect of mutation count on ( Fig.9A) tumor shedding and ( Fig. 9B ) input cfDNA, resulting in plasma tumor mutation burden (pTMB) being largely independent of these input metrics ( Fig. 9C and Fig.9D ). Fig.9A and Fig. 9B The distribution of mutation counts for each bin of either tumor dropout (approximately the maximum somatic MAF (max MAF)) or input volume (resulting in molecular coverage) is shown. Fig. 9C and Fig.9D The corrected TMB scores for the same bins are shown. The violin plots show the median and interquartile range (IQR). The black line shows the median trend.

[0312] 4. TMB Across Tumor Types

[0313] Fig. 10A and Fig. 10B is a graph showing the distribution of TMB in the entire cohort and between tumor types:( Fig. 10A ) Across the entire cohort, TMB scores showed a long-tailed distribution, matching previous observations in tissue- and plasma-based TMB, with a median of 10 muts / Mb across tumor types and an upper tertile of 14 muts / Mb (black line). Fig. 10B Median TMB varied among colorectal, lung, and prostate cancer samples, with trends consistent with previous tissue-based observations.

[0314] 5. TMB is independent of race

[0315] Fig.11A and Fig. 11B : is a graph showing that the median TMB does not vary between races: The samples were clustered using principal component analysis (PCA) of common germline mutations to define race-based clusters. The clusters were labeled using a set of samples of known race (Asian / Pacific Islander: A; Black / African American: B, Caucasian: W). For each race-based cluster, 23 tumor type-matched samples were randomly selected. Fig.11A The PCA clustering is shown, and Fig. 11B TMB scores are shown. Unlike some tissue TMB pipelines, median TMB was constant between clusters (A vs. W: p=0.78, B vs. W: 0.77, A vs. B: 0.99 using median comparisons based on nonparametric sampling).

[0316] 6. Correlation between TMB and oncogenic mutations

[0317] Fig.12It is a graph showing that TMB in lung samples (i.e., plasma-based TMB (pTMB)) varies by driver status: In lung samples, the median TMB in EGFR-driven lung tumors and ALK-driven lung tumors was lower (median 8muts / Mb, p<0.01 for the equivalent of the median) compared to all lung tumors (12muts / Mb), while the median TMB in tumors with KRAS or PIK3CA hotspot mutations was higher (14muts / Mb, p=0.04). Lung tumors with BRAF drive have a median TMB similar to all lung tumors (11muts / Mb, p=0.25). Loss of function (LOF) mutations in STK11, KEAP1, or PTEN are putative negative predictors of ICI response. The median TMB in tumors with these mutations (15muts / Mb, p<0.01) is slightly higher than all lung tumors, indicating that these latter events may be independent clinical biomarkers of TMB.

[0318] 7. TMB landscape with variable clonal architecture, chromosomal instability, and MSI-status

[0319] Fig.13A It is a diagram showing that the clonality of somatic mutations and chromosomal instability are highly variable in the entire TMB landscape: within the TMB score range observed in the cohort, there is a series of subclonal mutation scores. This is illustrated by 100 randomly selected tumor samples between tumor types, sorted by TMB score, clonal mutations (MAF≥10% of the maximum MAF of the sample) are black, and subclonal mutations (defined as MAF<10% of the maximum MAF of the sample) are light gray. The score of all mutations for subclones is shown in the bar below (black = high cloning; light gray = high subclone). Subclone scores are not related to TMB scores. Similarly, chromosomal instability, measured by the number of genes detected to be amplified (#CNV: displayed under subclone scores) or the group space score for diploids (diploid score: displayed under (#CNV)), is not related to TMB scores. Fig. 13B is a graph showing that MSI-high was detected in a subset of TMB-high samples.

[0320] in conclusion

[0321] Panel-based TMB scores can leverage synonymous and noncoding mutations to enhance the signal of exome-wide mutational burden. As more patient outcome data become available, TMB algorithms and orthogonal biomarkers of tumor genomic immunogenicity will be further developed to improve guidance of patient response to immunotherapy. Sequencing panels with high sensitivity for TMB across large panel spaces and the ability to detect copy number changes and MSI status will be important for biomarker development and clinical application.

[0322] Example 3: Adjusting TMB scores in samples with lower tumor fractions and lower coverage using the TMB correction model

[0323] Patient samples were processed and analyzed using a blood-based DNA assay developed by Guardant Health, Inc. (Redwood City, CA). 13 somatic mutations were identified in the sample through bioinformatics analysis. In this embodiment, only SNVs and insertions and deletions of somatic mutations were considered. In this embodiment, of the thirteen somatic mutations, three were excluded from the observed mutation counts because two of the three mutations were non-tumor-related mutations (e.g., clonal hematopoietic mutations) and the other mutation was a driver mutation. The observed mutation counts, maximum MAF, and coverage of the sample are provided as input parameters for the TMB correction model. The model estimates the expected mutation score (f), the upper limit of the 95% confidence level of the expected mutation score (f 上限 ), adjusted mutation counts and TMB scores. Here, the adjusted mutation counts are divided by the product of the exome calibration factor and the size of the genomic region analyzed (~1Mb in this example) to determine the TMB score. Table 6 shows the reported TMB scores from the TMB correction model.

[0324] Table 6

[0325]

[0326]

[0327] Example 4: Samples not evaluated using the TMB calibration model (low tumor score)

[0328] Patient samples were processed and analyzed using a blood-based DNA assay developed by Guardant Health, Inc. (Redwood City, CA). Through bioinformatics analysis, a somatic mutation was identified in the sample. In the present embodiment, only SNV and insertion / deletion were considered as somatic mutations. In addition, the maximum MAF of the sample was determined to be 0.1%. In the present embodiment, the maximum MAF was used as a tumor score. The tumor score of the sample was lower than the tumor score cutoff value. Therefore, the sample was not evaluated using the TMB correction model. Table 7 further summarizes the data of the present embodiment.

[0329] Table 7

[0330]

[0331] Although the foregoing disclosure has been described in some detail by way of illustration and example for the purpose of clarity and understanding, it will be apparent to those skilled in the art upon reading this disclosure that various changes may be made in form and detail without departing from the true scope of the disclosure and may be implemented within the scope of the appended claims. For example, all methods, systems, computer-readable media and / or component features, steps, elements or other aspects thereof may be used in various combinations.

[0332] All patents, patent applications, websites, other publications, accession numbers, etc. cited herein are incorporated by reference in their entirety for all purposes, to the extent that each individual item is specifically and individually indicated to be incorporated by reference. If different versions of a sequence are associated with an accession number at different times, it is meant to refer to the version associated with the accession number on the effective filing date of the present application. If applicable, the effective filing date means the earlier of the actual filing date or the filing date of the priority application that mentions the accession number. Similarly, if different versions of a publication, website, etc. are published at different times, it is meant to refer to the version most recently published on the effective filing date of the present application, unless otherwise indicated.

Claims

1. A computer-readable medium comprising non-transitory computer-executable instructions that, when executed by at least one electronic processor, perform a method for determining a tumor mutation burden (TMB) of a subject, the method comprising: (a) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from the subject; (b) determining the tumor fraction and / or coverage of said nucleic acid; (c) determining an expected mutation score (f) and / or an expected distribution of said expected mutation scores to generate an expected result using the following equation: Expected fraction of mutations called (f) = ∑ MAF (P(called mutation|MAF)*P(mutation in MAF)) where MAF is the mutant allele fraction, P(called mutation|MAF) is the probability of calling a mutation at a specific MAF and P(mutation at MAF) is the distribution of expected MAFs, where P(called mutation|MAF) depends on the tumor fraction and the coverage of the nucleic acid, wherein the distribution of expected MAFs is determined by adjusting for tumor fraction in the sample from an expected distribution of relative MAFs obtained from a control sample dataset comprising at least 500 control samples; (d) adjusting the observed mutation count given the expected outcome to generate an adjusted mutation count, and (e) Determine TMB using the adjusted mutation counts.

2. A computer-readable medium comprising non-transitory computer-executable instructions that, when executed by at least one electronic processor, perform a method for determining a tumor mutation burden (TMB) of a subject, the method comprising: (a) providing a sample from the subject; (b) amplifying nucleic acids in the sample to generate amplified nucleic acids; (c) sequencing the amplified nucleic acid to generate sequence information; (d) determining an observed mutation count from the sequence information; (e) determining the tumor fraction and / or coverage of said nucleic acid; (f) determining an expected mutation score (f) and / or an expected distribution of said expected mutation scores to generate an expected result using the following equation: Expected fraction of mutations called (f) = ∑ MAF (P(called mutation|MAF)*P(mutation in MAF)) where MAF is the mutant allele fraction, P(called mutation|MAF) is the probability of calling a mutation at a specific MAF and P(mutation at MAF) is the distribution of expected MAFs, where P(called mutation|MAF) depends on the tumor fraction and the coverage of the nucleic acid, wherein the distribution of expected MAFs is determined by adjusting for tumor fraction in the sample from an expected distribution of relative MAFs obtained from a control sample dataset comprising at least 500 control samples; (g) adjusting the observed mutation count given the expected outcome to generate an adjusted mutation count, and (e) Determine TMB using the adjusted mutation counts.

3. A computer readable medium comprising non-transitory computer executable instructions that, when executed by at least one electronic processor, perform a method of selecting one or more customized treatments to treat cancer in a subject, the method comprising: (a) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from the subject; (b) determining the tumor fraction and / or coverage of said nucleic acid; (c) determining an expected mutation score (f) and / or an expected distribution of said expected mutation scores to generate an expected result using the following equation: Expected fraction of mutations called (f) = ∑ MAF (P(called mutation|MAF)*P(mutation in MAF)) where MAF is the mutant allele fraction, P(called mutation|MAF) is the probability of calling a mutation at a specific MAF and P(mutation at MAF) is the distribution of expected MAFs, where P(called mutation|MAF) depends on the tumor fraction and the coverage of the nucleic acid, wherein the distribution of expected MAFs is determined by adjusting for tumor fraction in the sample from an expected distribution of relative MAFs obtained from a control sample dataset comprising at least 500 control samples; (d) adjusting the observed mutation count given the expected outcome to generate an adjusted mutation count; and (e) comparing the adjusted mutation count to one or more comparator results indexed with one or more treatments to identify one or more customized treatments for the subject.

4. A computer-readable medium comprising non-transitory computer-executable instructions that, when executed by at least one electronic processor, perform a method of treating cancer in a subject, the method comprising: (a) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from the subject; (b) determining the tumor fraction and / or coverage of said nucleic acid; (c) determining an expected mutation score (f) and / or an expected distribution of said expected mutation scores to generate an expected result using the following equation: Expected fraction of mutations called (f) = ∑ MAF (P(called mutation|MAF)*P(mutation in MAF)) where MAF is the mutant allele fraction, P(called mutation|MAF) is the probability of calling a mutation at a specific MAF and P(mutation at MAF) is the distribution of expected MAFs, where P(called mutation|MAF) depends on the tumor fraction and the coverage of the nucleic acid, wherein the distribution of expected MAFs is determined by adjusting for tumor fraction in the sample from an expected distribution of relative MAFs obtained from a control sample dataset comprising at least 500 control samples; (d) adjusting the observed mutation count given the expected outcome to generate an adjusted mutation count; (e) comparing the adjusted mutation count to one or more comparator results indexed with one or more treatments to identify one or more customized treatments for the subject; and (f) administering at least one identified customized treatment to the subject when there is a substantial match between the adjusted mutation count and the comparator result, thereby treating the cancer in the subject.

5. A computer readable medium comprising non-transitory computer executable instructions that, when executed by at least one electronic processor, perform a method of identifying one or more customized treatments for a subject, the method comprising: (a) determining an observed mutation count based on sequence information obtained from one or more nucleic acids in a sample from the subject; (b) determining the tumor fraction and / or coverage of said nucleic acid; (c) determining an expected mutation score (f) and / or an expected distribution of said expected mutation scores to generate an expected result using the following equation: Expected fraction of mutations called (f) = ∑ MAF (P(called mutation|MAF)*P(mutation in MAF)) where MAF is the mutant allele fraction, P(called mutation|MAF) is the probability of calling a mutation at a specific MAF and P(mutation at MAF) is the distribution of expected MAFs, where P(called mutation|MAF) depends on the tumor fraction and the coverage of the nucleic acid, wherein the distribution of expected MAFs is determined by adjusting for tumor fraction in the sample from an expected distribution of relative MAFs obtained from a control sample dataset comprising at least 500 control samples; (d) adjusting the observed mutation count given the expected outcome to generate an adjusted mutation count; (e) comparing the adjusted mutation count to one or more comparator results indexed with one or more treatments; and (f) identifying one or more customized treatments for the subject when there is a substantial match between the adjusted outcome and the comparator outcome.

6. The computer-readable medium of any one of claims 1-5, wherein the observed mutation count and / or the tumor score comprises a number of synonymous mutations, a number of non-synonymous mutations, and / or a number of non-coding mutations identified in the nucleic acid.

7. The computer readable medium of any one of claims 1-5, wherein the observed mutation count and / or the tumor score comprises a plurality of mutations selected from the group consisting of: a single nucleotide variant, an insertion or deletion, a copy number variant, a fusion, a transversion, a translocation, a frameshift, a duplication, and an epigenetic variant.

8. The computer-readable medium of any one of claims 1-5, wherein the observed mutation count and / or the tumor fraction comprises repeat expansions.

9. The computer-readable medium of any one of claims 1-5, wherein the observed mutation count and / or the tumor score excludes driver mutations and / or clonal hematopoietic-derived mutations.

10. The computer readable medium of any one of claims 1-5, comprising determining the observed mutation count using pooled evidence for one or more possible mutations below a detection limit for a given single nucleotide variant or a given insertion or deletion.

11. The computer-readable medium of any one of claims 1-5, comprising using the expected mutation score as an observed fraction of actual mutation counts.

12. The computer-readable medium of any one of claims 1-5, wherein the observed mutation count and / or the tumor score comprises a plurality of somatic mutations identified in the nucleic acid.

13. The computer-readable medium of claim 12, wherein the observed mutation count excludes one or more known cancer driver mutations and / or passenger mutations.

14. The computer readable medium of any one of claims 1-5, comprising comparing the sequence information to one or more reference sequences to identify the observed mutation count.

15. The computer-readable medium of claim 14, wherein the reference sequence comprises at least a subsequence of hg19 and / or hg38.

16. The computer-readable medium of any one of claims 1-5, wherein the tumor score comprises the maximum mutant allele fraction (MAF) of all somatic mutations identified in the nucleic acid.

17. The computer readable medium of any one of claims 1-5, wherein the tumor fraction is less than 5% of all nucleic acids in the sample.

18. The computer readable medium of any one of claims 1-5, wherein the tumor fraction is less than 4% of all nucleic acids in the sample.

19. The computer readable medium of any one of claims 1-5, wherein the tumor fraction is less than 3% of all nucleic acids in the sample.

20. The computer readable medium of any one of claims 1-5, wherein the tumor fraction is less than 2% of all nucleic acids in the sample.

21. The computer readable medium of any one of claims 1-5, wherein the tumor fraction is less than 1% of all nucleic acids in the sample.

22. The computer readable medium of any one of claims 1-5, wherein the tumor fraction is less than 0.5% of all nucleic acids in the sample.

23. The computer readable medium of any one of claims 1-5, wherein the tumor fraction is less than 0.2% of all nucleic acids in the sample.

24. The computer readable medium of any one of claims 1-5, wherein the tumor fraction is less than 0.1% of all nucleic acids in the sample.

25. The computer readable medium of any one of claims 1-5, wherein the tumor fraction is less than 0.05% of all nucleic acids in the sample.

26. The computer-readable medium of any one of claims 1-5, the method comprising identifying a plurality of unique cfDNA fragments comprising a given nucleotide position in the nucleic acid to determine the coverage.

27. The computer-readable medium of any one of claims 1-5, the method comprising identifying a median number of unique cell-free DNA molecules comprising a given nucleotide position in the nucleic acid to determine the coverage.

28. The computer-readable medium of any one of claims 1-5, wherein at a given nucleotide position in the nucleic acid present in the sample, the coverage is between 10 and 50,000 cfDNA fragments.

29. The computer-readable medium of any one of claims 1-5, wherein the expected mutation score and / or the expected distribution of the expected mutation scores comprises a 95% or greater confidence interval for the mutation score.

30. The computer-readable medium of any one of claims 1-5, the method comprising using an upper bound of a 95% confidence interval for the expected mutation score to generate a lower bound for the observed mutation count.

31. The computer readable medium of any one of claims 1-5, the method comprising calculating the probability of identifying a mutation in a given MAF in an expected mutant allele fraction (MAF) distribution to determine the expected mutation fraction.

32. The computer readable medium of claim 31, the method comprising multiplying the distribution of expected relative MAFs by the tumor fraction to generate a MAF.

33. The computer readable medium of claim 31 , wherein the distribution of expected MAF is calculated using the following binomial proportion confidence interval: and where f is the expected fraction of mutations called, n_true is the expected actual mutations, equal to the number of mutations observed given f, and z is the confidence level.

34. The computer-readable medium of claim 33, wherein if f is less than a threshold f , then TMB is not determined.

35. The computer-readable medium of claim 33, wherein if f is greater than a threshold f , then determine the TMB.

36. The computer readable medium of any one of claims 1-5, wherein the maximum MAF observed in the control sample data set comprises 0.5%, 1%, 2%, 5%, or 10%.

37. The computer readable medium of any one of claims 1-5, the method comprising fitting the relative MAF to a curve using the following equation: F = 1 / (1+(P_50 / relative-MAF) n ), where F is the cumulative distribution function, P_50 is the median relative MAF, Relative-MAF is the relative MAF, and n is an exponent that fits the relative distribution shape.

38. A computer-readable medium according to any of the preceding claims 1-5, the method comprising dividing the observed mutation count by the expected mutation score in the sample or an upper / lower limit of a confidence interval of the expected mutation score to generate an adjusted result.

39. The computer-readable medium of any one of claims 1-5, wherein the adjusted result comprises a number of mutations detected in the nucleic acid at a range of mutant allele fractions.

40. The computer-readable medium of any preceding claim 1-5, the method comprising dividing the observed mutation count by the expected result to generate an adjusted result.

41. The computer-readable medium of any of the preceding claims 1-5, wherein the adjusted result comprises a prediction of a highest possible actual mutation count.

42. The computer-readable medium of any of the preceding claims 1-5, wherein the adjusted result comprises a prediction of a lowest possible actual mutation count.

43. The computer-readable medium of any preceding claim 1-5, wherein the adjusted result comprises an adjusted mutation count.

44. The computer-readable medium of claim 43, wherein the adjusted mutation count is greater than or equal to the observed mutation count.

45. The computer-readable medium of any one of claims 1-5, the method comprising obtaining the sequence information from nucleic acid in a sample from the subject.

46. ​​The computer-readable medium of claim 45, wherein the sequence information is obtained from a targeted segment of the nucleic acid.

47. The computer readable medium of claim 46, wherein the targeted segments comprise between 1 and 100,000 different and / or overlapping genomic regions.

48. The computer readable medium of any one of claims 1-5, the method comprising obtaining a sample from the subject.

49. The computer readable medium of claim 48, wherein the sample is selected from the group consisting of tissue, blood, plasma, serum, sputum, urine, semen, vaginal fluid, feces, synovial fluid, cerebrospinal fluid, and saliva.

50. The computer readable medium of claim 48, wherein the sample comprises tissue.

51. The computer readable medium of claim 48, wherein the sample comprises blood, plasma, and / or serum.

52. The computer readable medium of claim 48, wherein the subject is a mammalian subject.

53. The computer-readable medium of claim 48, wherein the subject is a human subject.

54. The computer-readable medium of claim 48, wherein the nucleic acid comprises cell-free nucleic acid.

55. The computer-readable medium of claim 48, wherein the nucleic acid comprises cellular nucleic acid.

56. The computer readable medium of claim 48, wherein the nucleic acid comprises circulating tumor nucleic acid.

57. The computer-readable medium of claim 48, wherein the nucleic acid is obtained from circulating tumor cells.

58. The computer-readable medium of claim 48, wherein the nucleic acid comprises deoxyribonucleic acid and / or ribonucleic acid.

59. The computer-readable medium of claim 48, the method comprising amplifying at least one segment of a nucleic acid in the sample to generate at least one amplified nucleic acid.

60. The computer-readable medium of claim 59, the method comprising sequencing the amplified nucleic acid to generate the sequence information.

61. The computer readable medium of claim 60, the method comprising sequencing at least 50,000 nucleotides of the nucleic acid to generate sequence information.

62. The computer readable medium of claim 60, the method comprising sequencing at least 100,000 nucleotides of the nucleic acid to generate sequence information.

63. The computer readable medium of claim 60, the method comprising sequencing at least 150,000 nucleotides of the nucleic acid to generate sequence information.

64. The computer readable medium of claim 60, the method comprising sequencing at least 200,000 nucleotides of the nucleic acid to generate sequence information.

65. The computer readable medium of claim 60, the method comprising sequencing at least 250,000 nucleotides of the nucleic acid to generate sequence information.

66. The computer readable medium of claim 60, the method comprising sequencing at least 500,000 nucleotides of the nucleic acid to generate sequence information.

67. The computer readable medium of claim 60, the method comprising sequencing at least 750,000 nucleotides of the nucleic acid to generate sequence information.

68. The computer readable medium of claim 60, the method comprising sequencing at least 1,000,000 nucleotides of the nucleic acid to generate sequence information.

69. The computer readable medium of claim 60, the method comprising sequencing at least 1,500,000 nucleotides of the nucleic acid to generate sequence information.

70. The computer readable medium of claim 60, the method comprising sequencing at least 2,000,000 nucleotides of the nucleic acid to generate sequence information.

71. The computer readable medium of claim 60 or 61, wherein the sequencing is selected from the group consisting of targeted sequencing, intron sequencing, exome sequencing, and whole genome sequencing.

72. The computer readable medium of any one of claims 3-5, wherein the customized therapy comprises at least one immunotherapy.

73. The computer readable medium of claim 72, wherein the immunotherapy comprises at least one checkpoint inhibitor antibody.

74. The computer-readable medium of claim 72, wherein the immunotherapy comprises antibodies against PD-1, PD-2, PD-L1, PD-L2, CTLA-40, OX40, B7.1, B7He, LAG3, CD137, KIR, CCR5, CD27, or CD40.

75. The computer readable medium of claim 72, wherein the immunotherapy comprises administering a pro-inflammatory cytokine against at least one tumor type.

76. The computer-readable medium of claim 72, wherein the immunotherapy comprises administering T cells directed against at least one tumor type.

77. The computer readable medium of any one of claims 3-5, wherein the cancer comprises at least one tumor type selected from the group consisting of biliary tract cancer, urothelial cancer, glioma, astrocytoma, breast cancer, cervical cancer, colorectal cancer, gastrointestinal stromal tumor, esophageal cancer, ocular melanoma, renal cell carcinoma, leukemia, liver cancer, lung cancer, mesothelioma, B-cell lymphoma, mantle cell lymphoma, T-cell lymphoma, multiple myeloma, nasopharyngeal carcinoma, neuroblastoma, oral cancer, osteosarcoma, ovarian cancer, pancreatic cancer, pseudopapillary tumor, acinar cell carcinoma, prostate cancer, skin cancer, small intestine cancer, stomach cancer, and uterine cancer.

78. The computer readable medium of any one of claims 3-5, wherein the cancer comprises at least one tumor type selected from the group consisting of bladder cancer, bile duct cancer, rectal cancer, colon cancer, gallbladder cancer, and endometrial cancer.

79. The computer readable medium of any one of claims 3-5, wherein the cancer comprises hereditary non-polyposis colorectal cancer.

80. The computer readable medium of any one of claims 3-5, wherein the cancer comprises at least one tumor type selected from the group consisting of transitional cell carcinoma, brain cancer, metaplastic carcinoma, cervical squamous cell carcinoma, colorectal adenocarcinoma, endometrial stromal sarcoma, esophageal squamous cell carcinoma, gallbladder adenocarcinoma, clear cell renal cell carcinoma, Wilms tumor, acute lymphocytic leukemia, acute myeloid leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic myelomonocytic leukemia, hepatoepithelial carcinoma, hepatoblastoma, non-small cell lung cancer, non-Hodgkin lymphoma, diffuse large B-cell lymphoma, precursor T-lymphoblastic lymphoma / leukemia, oral squamous cell carcinoma, pancreatic ductal adenocarcinoma, prostate adenocarcinoma, melanoma, gastric epithelial carcinoma, uterine sarcoma.

81. The computer readable medium of any one of claims 3-5, wherein the cancer comprises at least one tumor type selected from the group consisting of: esophageal adenocarcinoma, hepatoma, peripheral T-cell lymphoma, and malignant melanoma.

82. The computer readable medium of any one of claims 3-5, wherein the cancer comprises at least one tumor type selected from the group consisting of: uveal melanoma, hepatocellular carcinoma, and cutaneous melanoma.

83. The computer-readable medium of any one of claims 1-5, wherein the sequence information comprises sequence reads of the nucleic acid generated by a nucleic acid sequencer.

84. The computer-readable medium of claim 83, wherein the nucleic acid sequencer performs single molecule sequencing, synthesis sequencing, ligation sequencing, or hybridization sequencing on the nucleic acid to generate sequencing reads.

85. The computer-readable medium of claim 83, wherein the nucleic acid sequencer performs pyrophosphate sequencing, nanopore sequencing, or semiconductor sequencing on the nucleic acid to generate sequencing reads.

86. The computer readable medium of any one of claims 1-5, further comprising selectively enriching one or more regions from the genome or transcriptome prior to sequencing.

87. The computer-readable medium of claim 86, further comprising amplifying the selectively enriched region prior to sequencing.

88. The computer-readable medium of any one of claims 1-5, wherein the sequence information is obtained from a targeted segment of the nucleic acid, wherein the targeted segment is obtained by selectively enriching one or more regions from a genome or transcriptome prior to sequencing.

89. The computer-readable medium of claim 88, further comprising amplifying the obtained targeted segment prior to sequencing.

90. The computer readable medium of any one of claims 1-5, the method further comprising attaching one or more adaptors comprising a molecular barcode to the nucleic acid prior to sequencing.

91. The computer-readable medium of claim 90, wherein the nucleic acids are uniquely tagged.

92. The computer-readable medium of claim 90, wherein the nucleic acids are non-uniquely tagged.

93. The computer readable medium of claim 90, wherein the adaptor comprises between 2 and 1,000,000 molecular barcodes.

94. The computer-readable medium of claim 90, the method comprising randomly attaching an adaptor comprising a molecular barcode to each end of the nucleic acid.

95. The computer-readable medium of claim 90, wherein the adaptor is attached to the nucleic acid by blunt-end ligation or sticky-end ligation.

96. The computer readable medium of claim 90, wherein the adaptor is a T-tail adaptor and / or a C-tail adaptor.

97. The computer-readable medium of any one of claims 1-5, the method further comprising grouping the sequence reads into sequence read families, each family comprising sequence reads generated from nucleic acids in the sample.

98. The computer-readable medium of any one of claims 1-5, the method further comprising generating a report in an electronic format providing one or more TMB scores.

99. The computer readable medium of any one of claims 1-5, the method further comprising classifying the sample as a TMB-high sample.

100. The computer-readable medium of any one of claims 1-5, wherein the mutation count is determined in a genomic region comprising the following genes: ABL1, ABL2, ACVR1B, ACVR2A, ADARB2, ADGRA2, ADGRG4, AFDN, AKT1, AKT1S1, AKT2, AKT3, ALB, ALK, ALOX12B, ALOX15B, ALOX5, AMER1, APC, APEX1, AR, ARAF, ARFRP1, ARHGAP35, ARID1A, ARID1B, ARID2, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXI N1, AXIN2, AXL, B2M, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BIRC5, BLM, BRAF, BRCA1, BRCA2, BRD2, BRD3, BRD4, BRIP1, BTG1, BTG 2. BTK, BUB1B, CARD11, CASP8, CBFB, CBL, CBLB, CCND1, CCND2, CCND3, CCNE1, CD274, CD79A, CD79B, CDC7, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CEP295, CHEK1, CHEK2, CIC, CNOT3, CREBBP, CRKL, CRTC1, CSF1R, CTCF, CTLA4, CTNNA1, CTNNB1, CUL3, CUX1, CY LD, DAXX, DDIT3, DDR1, DDR2, DEPDC5, DEPTOR, DICER1, DLL4, DNMT3A, DOT1L, DYRK2, E2F3, ECT2L, EGFR, EIF1AX, EIF4A1, EIF4A2, EIF4A3, EIF4B, EIF4E, EIF4E2, ELF3, EML4, EMSY, EP300, EPCAM, EPHA3, EPHA5, EPHA7, EPHB1, ERBB2, ERBB3, ERBB4, ERCC1, ERCC2, ERCC3, ERCC4, ERCC5, ERCC6, ERCC8, ERG, ERR FI1, ESR1, ETV1, ETV4, ETV5, ETV6, EWSR1, EXO1, EZH2, FAAP100, FAAP20, FAAP24, FAM175A, FAM46C, FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG,<h2 style=";text-align:left;direction:ltr">FANCI、FANCL、FANCM、FAS、FAT1、FBXW7、FEN1、FGF10、FGF14、FGF19、FGF23、 FGF3、FGF4、FGF6、FGFR1、FGFR2、FGFR3、FGFR4、FH、FLCN、FLT1、FLT3、FLT4、F OXA1、FOXL2、FOXO1、FOXP1、FRS2、FUBP1、FZD1、FZD10、FZD2、FZD3、FZD4、FZ D5、FZD6、FZD7、FZD8、FZD9、GAS6、GATA1、GATA2、GATA3、GATA6、GEN1、GID4、G NA11, GNA13, GNAQ, GNAS, GRIN2A, GSK3B, H3F3A, HDAC2, HELQ, HES1, HEY1, HEYL, HGF, HIST3H3, HNF1A, HRAS, HSP90AA1, IDH1, IDH2, IDO1, IFNG, IFNGR1 IFNGR2, IGF1, IGF1R, IGF2, IGF2R, IKBKE, IKZF1, IL2RG, IL7R, INHBA, INPP4B, IRF1, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KAT6A, KDM4A, KDM5A, KDM5B, KD M5C、KDM6A、KDR、KEAP1、KIT、KLHL6、KMT2A、KMT2D、KNSTRN、KRAS、LGR4、LGR 5、LGR6、LIG1、LIG4、LMO1、LRP1B、LRP2、LRP5、LRP6、MAD2L2、MAP2K1、MAP2K2 、MAP2K4、MAP3K1、MAP4K3、MAPK1、MAPK3、MAPKAP1、MAX、MCL1、MDC1、MDM2、M DM4、MED12、MEF2B、MEN1、MERTK、MET、MITF、MLH1、MLH3、MLST8、MPL、MRAS、MR E11、MSH2、MSH3、MSH6、MTOR、MUTYH、MYB、MYC、MYCL、MYCN、MYD88、NBN、NF1、 NF2、NFE2L2、NFKBIA、NHEJ1、NKX2-1、NOTCH1、NOTCH2、NOTCH3、NOTCH4、NPM1 、NPRL2、NPRL3、NRAS、NSD1、NTRK1、NTRK2、NTRK3、NUMB、NUP93、NUTM1、PAK3、 PALB2、PARG、PARP1、PARP2、PAX5、PBRM1、PCDH15、PDCD1、PDCD1LG2、PDGFRA、<h2 style=";text-align:left;direction:ltr">PDGFRB、PDK1、PHF6、PIAS4、PIK3C2B、PIK3CA、PIK3CB、PIK3CD、PIK3CG、 PIK3R1、PIK3R2、PIK3R3、PIM1、PIN1、PKM、PLEKHS1、PMS1、PMS2、POLD1、 POLE、POLH、POLQ、POU2F2、PPARG、PPM1D、PPP2CA、PPP2R1A、PPP2R2A、PP P3CA、PPP6C、PRDM1、PREX1、PREX2、PRKAR1A、PRKCI、PRKDC、PTCH1、PTEN 、PTPN11、PTPRD、RAC1、RAD18、RAD21、RAD50、RAD51、RAD51B、RAD51C、RA D51D、RAD52、RAD54L、RAF1、RARA、RASA1、RB1、RBM10、RET、REV3L、RGS1、 RHEB、RHOA、RHOB、RICTOR、RIT1、RNF43、ROBO1、ROBO2、ROS1、RPA1、RPS2 7A、RPS6KA3、RPS6KB1、RPTOR、RRAGC、RSPO1、RSPO4、RUNX1、RUNX1T1、SD HB、SDHC、SDHD、SESN2、SETD2、SF3B1、SHFM1、SLC34A2、SLFN11、SLIT2、S MAD2、SMAD3、SMAD4、SMARCA2、SMARCA4、SMARCB1、SMO、SOCS1、SOCS3、SO S1, SOX10, SOX2, SOX9, SPEN, SPOP, SRC, SRSF2, SRY, STAG2, STAT3, STAT4, STK11, STK19, SUFU, SYK, TBC1D7, TBX3, TEK, TERT, TET2, TGFBR2, TMP RSS2、TNFAIP3、TNFRSF14、TNFRSF1A、TNK2、TOP1、TOPAZ1、TP53、TP53BP 1、TP63、TP73、TRAF3、TSC1、TSC2、TSHR、TSHZ2、TYRO3、U2AF1、UBE2T、US P9X、VEGFA、VHL、WEE1、WISP3、WRN、WT1、XBP1、XPA、XPC、XPO1、XRCC1、XR CC2、XRCC3、XRCC4、XRCC5、XRCC6、YAP1、ZNF217、ZNF703、ZNRF3、ZRSR2。、

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